selection bias

The Cult of Smart: How Our Broken Education System Perpetuates Social Injustice
by Fredrik Deboer
Published 3 Aug 2020

See education reform movement schools as key to American dream and plasticity of outcomes screening mechanisms for and selection bias and “Texas miracle” tracking and school quality See also college education; elementary schools; high schools science, technology, engineering, and math (STEM) scientific racism screening mechanisms charter schools as college education as for elite colleges for elite high schools housing prices and zoning as and international students and selection bias STEM classes as “weed out” classes seasteading Second Treatise of Government (Locke) “seed” metaphor of education segregation school wealth selection bias. See also survivorship bias self-actualization self-belief, messages of self-control self-made man mythology self-sufficiency September 11, 2001 sexism single nucleotide polymorphisms (SNPs) single-payer health insurance skills academic skills basic skills and constitutive moral luck and criterion referencing and degree creep as education’s value parenting skills skills unrelated to school social skills soft skills valued skills vocational skills Smith, Adam social hierarchy social justice social media and alt-right Facebook influencers Instagram social promotion socialism being a person under socialism childbirth and parenting under college under and communism contribution to society under Democratic Socialists of America (DSA) elementary school under and equality genuine socialism high school under and mobility revolutionary socialism and single-payer health insurance and societal commitment to its members work and employment under young adulthood under See also communism; Marxism; reforms, realistic socioeconomics and achievement gaps housing prices and zoning morality of social mobility of pundits and journalists soft skills Southwood, Ben special-needs students appropriate programs for and mental health needs and standardized testing standardized curriculum argument for looser standards Common Core standardized testing ACT College Learning Assessment (CLA) and Common Core and norm referencing SAT as screening mechanism for colleges as screening mechanism for private high schools and special-needs students Stanford Achievement Test.

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If academic performance tends to be so stable over the course of life, why do so many speak with great confidence about the power of good schooling? To a striking degree, our misapprehensions about the power of schooling to change the world stem from a simple but powerful kind of fallacy: the failure to recognize selection bias. Selection Bias: The First Mover in Education Metrics In the social sciences, we want to control for as many variables as we can. When we try to find associations, and especially when we want to prove causation, we need to make sure that the observed difference between groups is the product of our variable of interest.

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Failure to understand these systematic differences in population formation is the basis of selection bias. Most people fail to take these conditions into account even when they should be obvious. At Purdue, my doctoral institution, there is a large Chinese student population. I always chuckled to hear domestic students say, “Chinese people are all so rich!” It didn’t seem to occur to them that a school that costs better than $40,000 a year for international students acted as a natural screen to exclude the vast number of Chinese people who live in deep poverty. But I too would sometimes fall prey to this kind of selection bias. I had to take care to remind myself that my 8:30 a.m. writing classes weren’t going so much better than my 2:30 p.m. classes because I was somehow a better teacher in the mornings, but because the students who would sign up for an 8:30 a.m. class were probably the most motivated and prepared.

Advances in Financial Machine Learning
by Marcos Lopez de Prado
Published 2 Feb 2018

If you only publish those positive results, and hide the rest, your audience will not be able to deduce that these results are false positives, a statistical fluke. This phenomenon is called “selection bias.” Can you imagine one procedure to prevent this? What if we split the dataset in three sets: training, validation, and testing? The validation set is used to evaluate the trained parameters, and the testing is run only on the one configuration chosen in the validation phase. In what case does this procedure still fail? What is the key to avoiding selection bias? Bibliography Bharat Rao, R., G. Fung, and R. Rosales (2008): “On the dangers of cross-validation: An experimental evaluation.”

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If you do that, the chances of finding a false discovery will drop substantially, but still they will not be zero. 11.5 A Few General Recommendations Backtest overfitting can be defined as selection bias on multiple backtests. Backtest overfitting takes place when a strategy is developed to perform well on a backtest, by monetizing random historical patterns. Because those random patterns are unlikely to occur again in the future, the strategy so developed will fail. Every backtested strategy is overfit to some extent as a result of “selection bias”: The only backtests that most people share are those that portray supposedly winning investment strategies. How to address backtest overfitting is arguably the most fundamental question in quantitative finance.

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You go ahead and present only the result with the higher Sharpe ratio, arguing that a strategy with a shorter warm-up is more realistic. Is this selection bias? Your strategy achieves a Sharpe ratio of 1.5 on a WF backtest, but a Sharpe ratio of 0.7 on a CV backtest. You go ahead and present only the result with the higher Sharpe ratio, arguing that the WF backtest is historically accurate, while the CV backtest is a scenario simulation, or an inferential exercise. Is this selection bias? Your strategy produces 100,000 forecasts over time. You would like to derive the CPCV distribution of Sharpe ratios by generating 1,000 paths.

Calling Bullshit: The Art of Scepticism in a Data-Driven World
by Jevin D. West and Carl T. Bergstrom
Published 3 Aug 2020

WHAT YOU SEE DEPENDS ON WHERE YOU LOOK If you study one group and assume that your results apply to other groups, this is extrapolation. If you think you are studying one group, but do not manage to obtain a representative sample of that group, this is a different problem. It is a problem so important in statistics that it has a special name: selection bias. Selection bias arises when the individuals that you sample for your study differ systematically from the population of individuals eligible for your study. For example, suppose we want to know how often students miss class sessions at the University of Washington. We survey the students in our Calling Bullshit class on a sunny Friday afternoon in May.

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While accurate, you’re unlikely to hear a talking reptile say something like this in a Super Bowl commercial.*3 In all of these cases, the insurers presumably know that selection bias is responsible for the favorable numbers they are able to report. Smart consumers realize there is something misleading about the marketing, even if it isn’t quite clear what that might be. But sometimes the insurance companies themselves can be caught unaware. An executive at a major insurance firm told us about one instance of selection bias that temporarily puzzled his team. Back in the 1990s, his employer was one of the first major agencies to sell insurance policies online.

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THE MORTAL DANGER OF MUSICIANSHIP In a clinical trial designed to assess the severity of the side effects of a certain medication, the initial sample of patients may be random, but individuals who suffer side effects may be disproportionately likely to drop out of the trial and thus not be included in the final analysis. This is data censoring, a phenomenon closely related to selection bias. Censoring occurs when a sample may be initially selected at random, without selection bias, but a nonrandom subset of the sample doesn’t figure into the final analysis. * * * — LET’S DIVE RIGHT INTO an example. In March 2015, a striking graph made the rounds on social media. The graph, from a popular article about death rates for musicians, looked somewhat like the figure below and seems to reveal a shocking trend.

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking
by Foster Provost and Tom Fawcett
Published 30 Jun 2013

ad impressions, Example: Targeting Online Consumers With Advertisements adding variables to functions, Example: Overfitting Linear Functions advertising, Example: Targeting Online Consumers With Advertisements agency, Machine Learning and Data Mining alarms, Evaluating Classifiers algorithms clustering, Nearest Neighbors Revisited: Clustering Around Centroids data mining, From Business Problems to Data Mining Tasks k-means, Nearest Neighbors Revisited: Clustering Around Centroids modeling, A General Method for Avoiding Overfitting Amazon, The Ubiquity of Data Opportunities, Data Science, Engineering, and Data-Driven Decision Making, From Big Data 1.0 to Big Data 2.0, Data and Data Science Capability as a Strategic Asset, Similarity, Neighbors, and Clusters Borders vs., Achieving Competitive Advantage with Data Science cloud storage, Thinking Data-Analytically, Redux data science services provided by, Thinking Data-Analytically, Redux historical advantages of, Formidable Historical Advantage analysis counterfactual, From Business Problems to Data Mining Tasks learning curves and, Learning Curves analytic engineering, Decision Analytic Thinking II: Toward Analytical Engineering–From an Expected Value Decomposition to a Data Science Solution churn example, Our Churn Example Revisited with Even More Sophistication–From an Expected Value Decomposition to a Data Science Solution expected value decomposition and, From an Expected Value Decomposition to a Data Science Solution–From an Expected Value Decomposition to a Data Science Solution incentives, assessing influence of, Assessing the Influence of the Incentive–Assessing the Influence of the Incentive providing structure for business problem/solutions with, The Expected Value Framework: Decomposing the Business Problem and Recomposing the Solution Pieces–The Expected Value Framework: Decomposing the Business Problem and Recomposing the Solution Pieces selection bias, A Brief Digression on Selection Bias–A Brief Digression on Selection Bias targeting best prospects with, Targeting the Best Prospects for a Charity Mailing–A Brief Digression on Selection Bias analytic skills, software skills vs., Implications for Managing the Data Science Team analytic solutions, Data Mining and Data Science, Revisited analytic techniques, Other Analytics Techniques and Technologies–Answering Business Questions with These Techniques, Decision Analytic Thinking I: What Is a Good Model?

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O., What Data Can’t Do: Humans in the Loop, Revisited R Ra, Sun, Example: Jazz Musicians ranking cases, classifying vs., Visualizing Model Performance–Example: Performance Analytics for Churn Modeling ranking variables, Supervised Segmentation reasoning, Similarity, Neighbors, and Clusters Recall metric, Costs and benefits Receiver Operating Characteristics (ROC) graphs, ROC Graphs and Curves–ROC Graphs and Curves area under ROC curves (AUC), The Area Under the ROC Curve (AUC) in KDD Cup churn problem, Example: Performance Analytics for Churn Modeling–Example: Performance Analytics for Churn Modeling recommendations, Similarity, Neighbors, and Clusters Reddit, Why Text Is Important regional distribution centers, grouping/associations and, Co-occurrences and Associations: Finding Items That Go Together regression, From Business Problems to Data Mining Tasks, From Business Problems to Data Mining Tasks, Similarity, Neighbors, and Clusters building models for, Business Understanding classification and, From Business Problems to Data Mining Tasks ensemble methods and, Bias, Variance, and Ensemble Methods least squares, Regression via Mathematical Functions logistic, Example: Overfitting Linear Functions ridge, * Avoiding Overfitting for Parameter Optimization supervised data mining and, Supervised Versus Unsupervised Methods supervised segmentation and, Selecting Informative Attributes regression modeling, Generalizing Beyond Classification regression trees, Supervised Segmentation with Tree-Structured Models, Bias, Variance, and Ensemble Methods regularization, * Avoiding Overfitting for Parameter Optimization, Summary removing missing values, Data Preparation repetition, Data Science, Engineering, and Data-Driven Decision Making requirements, Data Preparation responders, likely vs. not likely, Using Expected Value to Frame Classifier Use retrieving, Similarity, Neighbors, and Clusters retrieving neighbors, Regression Reuters news agency, Example: Clustering Business News Stories ridge regression, * Avoiding Overfitting for Parameter Optimization root-mean-squared error, Generalizing Beyond Classification S Saint Magdalene single malt scotch, * Using Supervised Learning to Generate Cluster Descriptions Scapa single malt scotch, Understanding the Results of Clustering Schwartz, Henry, Stepping Back: Solving a Business Problem Versus Data Exploration scoring, From Business Problems to Data Mining Tasks search advertising, display vs., Example: Targeting Online Consumers With Advertisements search engines, Why Text Is Important second-layer models, Nonlinear Functions, Support Vector Machines, and Neural Networks segmentation creating the best, Selecting Informative Attributes supervised, Clustering unsupervised, Stepping Back: Solving a Business Problem Versus Data Exploration selecting attributes, Introduction to Predictive Modeling: From Correlation to Supervised Segmentation informative variables, Supervised Segmentation variables, Introduction to Predictive Modeling: From Correlation to Supervised Segmentation selection bias, A Brief Digression on Selection Bias–A Brief Digression on Selection Bias semantic similarity, syntactic vs., The news story clusters separating classes, Example: Overfitting Linear Functions sequential backward elimination, A General Method for Avoiding Overfitting sequential forward selection (SFS), A General Method for Avoiding Overfitting service usage, From Business Problems to Data Mining Tasks sets, Bag of Words Shannon, Claude, Selecting Informative Attributes Sheldon Cooper (fictional character), Example: Evidence Lifts from Facebook “Likes” sign consistency, in cost-benefit matrix, Costs and benefits Signet Bank, Data and Data Science Capability as a Strategic Asset, From an Expected Value Decomposition to a Data Science Solution Silver Lake, Term Frequency Silver, Nate, Evaluation, Baseline Performance, and Implications for Investments in Data similarity, Similarity, Neighbors, and Clusters–* Using Supervised Learning to Generate Cluster Descriptions applying, Example: Whiskey Analytics calculating, The Fundamental Concepts of Data Science clustering, Clustering–The news story clusters cosine, * Other Distance Functions data exploration vs. business problems and, Stepping Back: Solving a Business Problem Versus Data Exploration–Stepping Back: Solving a Business Problem Versus Data Exploration distance and, Similarity and Distance–Similarity and Distance heterogeneous attributes and, Heterogeneous Attributes link recommendation and, Link Prediction and Social Recommendation measuring, Similarity and Distance nearest-neighbor reasoning, Nearest-Neighbor Reasoning–* Combining Functions: Calculating Scores from Neighbors similarity matching, From Business Problems to Data Mining Tasks similarity-moderated classification (equation), * Combining Functions: Calculating Scores from Neighbors similarity-moderated regression (equation), * Combining Functions: Calculating Scores from Neighbors similarity-moderated scoring (equation), * Combining Functions: Calculating Scores from Neighbors Simone, Nina, Example: Jazz Musicians skew, Problems with Unbalanced Classes Skype Global, Term Frequency smoothing, Probability Estimation social recommendations, Link Prediction and Social Recommendation–Link Prediction and Social Recommendation soft clustering, Profiling: Finding Typical Behavior software development, Implications for Managing the Data Science Team software engineering, data science vs., A Firm’s Data Science Maturity software skills, analytic skills vs., Implications for Managing the Data Science Team Solove, Daniel, Privacy, Ethics, and Mining Data About Individuals solution paths, changing, Data Understanding spam (target class), Example: Targeting Online Consumers With Advertisements spam detection systems, Example: Targeting Online Consumers With Advertisements specified class value, Supervised Versus Unsupervised Methods specified target value, Supervised Versus Unsupervised Methods speech recognition systems, Thinking Data-Analytically, Redux speeding up neighbor retrieval, Computational efficiency Spirited Away, Example: Evidence Lifts from Facebook “Likes” spreadsheet, implementation of Naive Bayes with, Evidence in Action: Targeting Consumers with Ads spurious correlations, * Example: Why Is Overfitting Bad?

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Others may give $100 and then feel they need not donate for a while, ignoring many subsequent campaigns. The result would be that those who happened to donate in some past campaign will be biased towards those who donate less. Fortunately, there are data science techniques to help modelers deal with selection bias. They are beyond the scope of this book, but the interested reader might start by reading (Zadrozny & Elkan, 2001; Zadrozny, 2004) for an illustration of dealing with selection bias in this exact donation solicitation case study. Our Churn Example Revisited with Even More Sophistication Let’s return to our example of churn and apply what we’ve learned to examine it data-analytically.

Don't Trust Your Gut: Using Data to Get What You Really Want in LIfe
by Seth Stephens-Davidowitz
Published 9 May 2022

(Think of the cliche of the starving artist.) Also, the data tells us that most sexy businesses, such as record stores, fold quickly. So, what should we make of the fact that 12.5 percent of companies that consist of “independent artists, writers, and performers” have rich owners? This is largely due to something called selection bias, an important bias that must be considered anytime that you analyze data. Most independent artists, writers, and performers don’t have enough success that they turn themselves into a business for tax purposes. These struggling creatives, who never make any real profit, are not included in the data.

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If you added all these creatives, the odds of reaching big success as a creative would go way down. (This bias is much less of an issue for other businesses, in which a much higher percentage of people who start their own enterprise in the field will incorporate as a business.) That said, even if selection bias artificially jacks up the odds of success of independent creatives, the true odds of having huge success in this field may have been higher than I had guessed. Sure, becoming rich as a creative is a long shot. But I might have guessed it was a 1-in-100,000 long shot. The odds may be less long than that.

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Further, somewhere in the back of my mind, I hear the cool kids from high school saying, “While you nerds were spending years applying for grants, designing questionnaires, and coding your apps to uncover that sex is enormously pleasurable, we were busy, ya know, having sex.” Touché. But, when you pause to think about Mappiness’s methodology, the popularity of sex in the dataset is actually surprising. Recall that Mappiness only collects data on people willing to answer their survey the moment they hear a ping.* There is what statisticians call a selection bias here: the only sex participants in the Mappiness data sample were those willing to stop and answer the question. It is safe to assume that people in the midst of heart-pounding, mind-blowing, furniture-shaking, floor-rattling, scream-inducing, neighbor-awakening sex were able to tune out the little ping from Mappiness.

Super Thinking: The Big Book of Mental Models
by Gabriel Weinberg and Lauren McCann
Published 17 Jun 2019

The smokers in the study would therefore be those who selected to continue smoking, which can introduce a bias called selection bias. With selection bias, there is no guarantee that the study has isolated smoking to be the only difference between these groups. So if there is a difference detected at the end of the study, it cannot be easily determined how much smoking contributed to this difference. For instance, women who choose to continue smoking during their pregnancy against the advice of doctors may similarly make other medically questionable choices, which could drive adverse outcomes. Selection bias can also occur when a sample is selected that is not representative of the broader population of interest, as with online reviews.

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Essentially, you must be really careful when drawing conclusions based on nonrandom experiments. The Dilbert cartoon above pokes fun at the selection bias inherent in a lot of the studies reported in the news. A similar selection bias occurs with parents and school choice for their kids. Parents understandably want to give their kids a leg up and will often move or pay to send their kids to “better schools.” However, is the school better because there are better teachers or because the students are better prepared due to their parents’ financial means and interest in education? Selection bias likely explains some significant portion of these schools’ better test scores and college admissions.

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Selection bias likely explains some significant portion of these schools’ better test scores and college admissions. Another type of selection bias, common to surveys, is nonresponse bias, which occurs when a subset of people don’t participate in an experiment after they are selected for it, e.g., they fail to respond to the survey. If the reason for not responding is related to the topic of the survey, the results will end up biased. For instance, let’s suppose your company wants to understand whether it has a problem with employee motivation. Like many companies, you might choose to study this potential problem via an employee engagement survey. Employees missing the survey due to a scheduled vacation would be random and not likely to introduce bias, but employees not filling it out due to apathy would be nonrandom and would likely bias the results.

The Fair Trade Scandal: Marketing Poverty to Benefit the Rich
by Ndongo Sylla
Published 21 Jan 2014

Broadly speaking, these two publications argue that there is a slight improvement for producer 115 Sylla T02779 01 text 115 28/11/2013 13:04 the fair trade scandal organisations that have been fortunate enough to sell, but this impact is all but exceptional: ‘Better but not great’, to quote the actual title of the Jaffee (2009) article. However, these results should be interpreted with caution due to the ‘selection bias’ contained in this type of literature. The selection bias problem As a general rule, impact studies have been based on the assumption that the implementation of Fair Trade experienced a 100 per cent success rate. To assess its benefits, these studies focused on organisations for which some of the FT promises could be seen to have been fulfilled.

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The interlocutor in question has taken on many guises: Christian missionaries, agricultural technicians, representatives of Fair Trade organisations, NGOs, development agencies, etc.25 The selection bias is the Achilles heel of the literature on the impact of Fair Trade. The results of each study may prove interesting when taken in isolation, but they can in no way be generalised. Due to their individualistic methodology, modern econometric techniques simply cannot correct this selection bias. They certainly enable the pairing of a treatment group with another similar group from the point of view of the characteristics of its members. But this overlooks the fact that this selection borrows from social processes and that the whole is not the sum of its parts.

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(Nelson and Pound, 2009: 10) Second illustration: The Impact of Fair Trade (Ruben, 2009) This book, whose results were included in the previous summary document, starts by underscoring the methodological weaknesses of most existing studies, namely that they do not conduct baseline studies, do away with the use of reference groups and do not take into account the possible selection bias involved in participation in the FT system. Several studies have tried to capture the impact of Fair Trade for local producers and households, but sound empirical evidence regarding social, economic and ecological impact remains scattered and sometimes contradictory. Due to the notable absence of base-line studies and reference groups, it remains difficult to precisely assess the welfare impact at household and cooperative level.

Understanding Asset Allocation: An Intuitive Approach to Maximizing Your Portfolio
by Victor A. Canto
Published 2 Jan 2005

These are somewhat surprising results based on the conventional wisdom—one would expect to find the opposite result, which would be a 60 percent allocation to large-cap stocks in both cases. The question now becomes whether 30 years is a long enough span to 26 UNDERSTANDING ASSET ALLOCATION generate a long-run result. If the sample period is not long enough to establish long-run returns, the results can suffer from sample-selection bias. Indeed, there are some reasons to question the impact of the sample period on the results. First, on theoretical grounds, one can argue that if markets are reasonably efficient, the market portfolio that buys the market should be on the efficient frontier—that owning a historically proven asset class mix is the best bet for every investor.

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My analysis suggests that, during the bulk of the 1970s and part of the 1980s, the economic environment favored value stocks over growth stocks. It is, therefore, not surprising to see that most empirical literature has found that value stocks outperform growth stocks. It seems increasingly clear, however, that the results reported in the literature suffer from sample-selection bias. Value stocks did well because of the economic policies adopted over the past three decades. If this is in fact the case—as I believe it is—there is no guarantee the overall economic environment in the future will favor value stocks at all times. My analysis suggests it behooves investors to pay attention to the economic environment and suggests growth stocks—when the environment warrants—can have an important impact on the total return of a style-based asset-allocation strategy. 30 UNDERSTANDING ASSET ALLOCATION Size Cycles Size cycles also matter.

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This could be dangerous, as the sampleselection bias could result in a strategic asset allocation mistakenly overweighing or underweighing some asset classes. Because the mean-reversion hypothesis—as most people articulate it—assumes random disturbances around the mean (assuming no fundamental change in the process generating the returns), the solution to the sample-selection bias is to use the longest time period possible.9 Table 2.11 Optimal allocation based on the Sharpe ratio produced by the historical returns: 1975–2004. Size Style Location Equity/Fixed 40 Small Large 80.0% 20.0% Value Growth 100.0% 0.0% USA Rest of the World 100.0% 0.0% Equity Fixed Income 60.0% 40.0% UNDERSTANDING ASSET ALLOCATION Figures 2.6 through 2.10 show that, when one looks at the relative performance of the various asset classes, distinct patterns emerge.

The Upside of Inequality
by Edward Conard
Published 1 Sep 2016

No surprise, these schools’ students score higher on tests, if for no other reason than selection bias. Higher scores make the schools more desirable to the most ambitious parents. Conscientious parents flock to apply. This further skews the pool to students with parents who apply to a large numbers of schools. No surprise, only fierce, no-excuses, KIPP-like charter schools and schools with lotteries appear to outperform their public school counterparts systematically.16 Just as propagandists are quick to overlook alternative explanations for the best results out of fifty states, they are also quick to overlook selection bias. In fact, they often seek out hidden selection bias to add apparent statistical significance to otherwise insignificant results.

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In fact, they often seek out hidden selection bias to add apparent statistical significance to otherwise insignificant results. Sloppy statistical analysis is the provenance of propaganda, especially in economics, where, unlike in science, it is seldom possible to compare experimental outcomes to carefully designed control groups or other counterfactuals. Selection bias is the scourge of science. Hence, science demands randomized double-blind trials—where neither the subjects nor the researchers know which group is the experimental group and which group is the counterfactual control group. But the very thing scientific experiments endeavor to overcome—selection bias—fiercely drives real-world outcomes.

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While their results, and the results of other charter schools that employ the fierce “no excuses” philosophy of KIPP, hold out guarded hope for implementing large-scale improvements without the need for new pedagogy, the alleged improvements are far less convincing than proponents of education reform would have the public believe. In any statistical sample, one has to be very concerned that experimental results stem not from the effect of the treatment, but from the selection of participants to receive the treatment—what statisticians call selection bias. This is especially true in education, where conscientious parents work hard to secure the best education for their children. Given the difficulties of gaining admission to many charter schools—having the interest, making the effort, and often winning one or more of several school lotteries—the pool of students seeking admission to charter schools skews heavily toward students with ambitious and conscientious parents.

Everydata: The Misinformation Hidden in the Little Data You Consume Every Day
by John H. Johnson
Published 27 Apr 2016

One statistician found that “ninety-seven per cent of all published psychological studies with statistically significant data found the effect they were looking for,” making it perhaps less likely that future studies would be able to replicate these results.37 The Journal of Epidemiology and Community Health published a paper finding no evidence that reduced street lighting at night increased traffic collisions or crime in England and Wales. But the authors (rightfully) acknowledged the possibility of selection bias—they didn’t get data from approximately one-third of the local authorities, and said, “It is possible that local authorities may have declined to participate because of expected or known increases in collisions or crime in their areas due to lighting changes.”38 Just because something is statistically significant doesn’t make all the other issues go away.

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But it’s the makeup of the sample that concerns us more than the sample size. Because the results appear to be based on a self-selected group of attorneys who responded to the survey, according to the Law360 articles. When you have a group of people opting into a study, there’s an opportunity for selection bias. The results may be biased toward those who chose to participate. Are the attorneys who responded any different from those who were too busy to answer, or chose not to respond for whatever reason—and would those differences be related to the survey’s findings? For example, is it possible that non-equity partners who are happy are also the busiest, and therefore didn’t have time to respond to the survey?

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Aggregated data—Individual data points combined together into groups (e.g., the total number of votes in a state are aggregated to determine who receives that state’s Electoral College votes) Average—A type of summary statistic (usually the mean, mode, or median) that describes the data in a single metric Big data—Data that’s too big for people to process without the use of sophisticated machinery or computing capacity, given its enormous volume Bivariate relationship—A fancy way of saying that there is a relationship between two (“bi”) variables (“variate”) (e.g., the price of your house is related to the number of bathrooms it has) Black swan event—Something that is highly improbable, yet has a massive impact when it occurs Causation—A relationship where it is determined that one factor causes another factor Cherry-picking—Choosing anecdotal examples from the data to make your point, while ignoring other data points that may contradict it Confidence interval—A way to measure the level of statistical certainty about results; typically expressed as a range of values, the confidence interval tells you the range of values within which you’re likely to see the estimate (assuming, of course, you have a random—and representative—sample) Confidence level—The term we use to determine how confident we are that we’re measuring the data correctly Confirmation bias—The tendency to interpret data in a way that reinforces your preconceptions Correlation—A type of statistical relationship between two variables, usually defined as positive (moving in the same direction) or negative (moving in opposite directions) Data—Information or facts Dependence—When one variable is said to be directly determined by another Deterministic forecast—A forecast for which you determine a precise outcome (e.g., it will rain tomorrow at 9 a.m. at my house) Economic impact—How much something is going to cost in terms of time, money, health, or other resources Estimate—A statistic capturing an inference about a population from a sample of data Everydata—The term we use to describe everyday data External validity—The extent to which the results from your sample can be extended to draw meaningful conclusions about the full population False positive—A situation in which the statistical forecast predicts an untrue outcome (e.g., your credit card company calls you suspecting a recent purchase you actually made was fraudulent) Forecast—A statement about the future; while forecast and prediction may have different meanings to specific groups of people (see chapter 8), we generally use them synonymously unless noted otherwise Forecast bias—The term used to describe when a prediction is consistently high (a positive forecast bias) or low (a negative bias) Inference—The process of making statistical conclusions about the data Magnitude—Essentially, the size of the effect Margin of error—A way to measure statistical uncertainty Mean—What most people think of when you say “average” (to get the mean, you add up all the values, then divide by the number of data points) Median—The middle value in a data set that has been rank ordered Misrepresentation—When data is portrayed in an inaccurate or misleading manner Mode—The data point (or points) most frequently found in your data Observation—Looking at one unit, such as a person, a price, or a day Odds—In statistics, the odds of something happening is the ratio of the probability of an outcome to the probability that it doesn’t occur (e.g., a horse’s statistical odds of winning a race might be ⅓, which means it is probable that the horse will win one out of every three races; in betting jargon, the odds are typically the reverse, so this same horse would have 2–1 odds against, which means it has a ⅔ chance of losing) Omitted variable—A variable that plays a role in a relationship, but may be overlooked or otherwise not included; omitted variables are one of the primary reasons why correlation doesn’t equal causation Outlier—A particular observation that doesn’t fit; it may be much higher (or lower) than all the other data, or perhaps it just doesn’t fall into the pattern of everything else that you’re seeing P-hacking—Named after p-values, p-hacking is a term for the practice of repeatedly analyzing data, trying to find ways to make nonsignificant results significant P-value—A way to measure statistical significance; the lower your p-value is, the less likely it is that the results you’re seeing are due to chance Population—The entire set of data or observations that you want to study and draw inferences about; statisticians rarely have the ability to look at the entire population in a study, although it could be possible with a small, well-defined group (e.g., the voting habits of all 100 U.S. senators) Prediction—See forecast Prediction error—A way to measure uncertainty in the future, essentially by comparing the predicted results to the actual outcomes, once they occur Prediction interval—The range in which we expect to see the next data point Probabilistic forecast—A forecast where you determine the probability of an outcome (e.g., there is a 30 percent chance of thunderstorms tomorrow) Probability—The likelihood (typically expressed as a percentage, fraction, or decimal) that an outcome will occur Proxy—A factor that you believe is closely related (but not identical) to another difficult-to-measure factor (e.g., IQ is a proxy for innate ability) Random—When an observed pattern is due to chance, rather than some observable process or event Risk—A term that can mean different things to different people; in general, risk takes into account not only the probability of an event, but also the consequences Sample—Part of the full population (e.g., the set of Challenger launches with O-ring failures) Sample selection—A potential statistical problem that arises when the way a sample has been chosen is directly related to the outcomes one is studying; also, sometimes used to describe the process of determining a sample from a population Sampling error—The uncertainty of not knowing if a sample represents the true value in the population or not Selection bias—A potential concern when a sample is comprised of those who chose to participate, a factor which may bias the results Spurious correlation—A statistical relationship between two factors that has no practical or economic meaning, or one that is driven by an omitted variable (e.g., the relationship between murder rates and ice cream consumption) Statistic—A numeric measure that describes an aspect of the data (e.g., a mean, a median, a mode) Statistical impact—Having a statistically significant effect of some undetermined size Statistical significance—A probability-based method to determine whether an observed effect is truly present in the data, or just due to random chance Summary statistic—Metric that provides information about one or more aspects of the data; averages and aggregated data are two examples of summary statistics Weighted average—An average calculated by assigning each value a weight (based on the value’s relative importance) NOTES Preface 1.

Investing Demystified: How to Invest Without Speculation and Sleepless Nights
by Lars Kroijer
Published 5 Sep 2013

However, ask the manager who has outperformed five years in a row (every 50th coin flipper …) and she will disagree with the argument that she was just lucky, even as some invariably are. Likewise some managers underperform the market several years in a row simply due to bad luck, but those disappear from the scene and thus introduce a selection bias as only the winners remain. This sometimes makes the industry appear more successful than it has been. Outside stock markets The discussion of edge is not exclusive to stock markets. You can have an investment edge in many areas other than the stock market and profit greatly from that edge, for example: Will Greece default on its loans?

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Combining high historical returns with low expected risk at the time made equity markets look very attractive at precisely the wrong moment. I understand why some criticise the expected return, but think that the length of data mitigates this. With hundreds of years of data across many countries (some have used only US data in the past, but that introduces selection bias by excluding markets that have performed poorly), incorporating great spectacular declines, great rises, and everything in between, I think historical data is the best guide to the kind of risk and return we can expect from equity markets in future. Practically speaking, investors have been unable to buy the whole world of equities for many years.

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People often use the US stock market for data analysis as it is not only the place with the most comprehensive data sets but it has, at least historically, dominated financial academic circles. Until only a couple of decades ago it was not as simple to get international data seamlessly and even if you did get the data it was not easy to analyse. One of the problems with using US-based data is the large selection bias that is introduced. The twentieth century was the American century and the stock market reflected this success. But just because we use data from a very successful century in a very successful geographical area does not mean that things will be like that in future. Imagine if you were an investor in the Russian stock markets or government bonds just before the 1917 revolution.

SuperFreakonomics
by Steven D. Levitt and Stephen J. Dubner
Published 19 Oct 2009

And, because it collects data in real time from all over the country, the system can serve as a Distant Early Warning Line for disease outbreaks or even bioterrorism. It also allows other, non-medical people—people like us, for instance—to repurpose its data to answer other kinds of questions, such as: who are the best and worst doctors in the ER? For a variety of reasons, measuring doctor skill is a tricky affair. The first is selection bias: patients aren’t randomly assigned to doctors. Two cardiologists will have two sets of clientele who may differ on many dimensions. The better doctor’s patients may even have a higher death rate. Why? Perhaps the sicker patients seek out the best cardiologist, so even if he does a good job, his patients are more likely to die than the other doctor’s.

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As word of his findings began to trickle out, he suddenly became, as he puts it, “clearly the most hated guy in the field.” List can at least be consoled by knowing that he is almost certainly correct. Let’s consider some of the forces that make such lab stories unbelievable. The first is selection bias. Think back to the tricky nature of doctor report cards. The best cardiologist in town probably attracts the sickest and most desperate patients. So if you’re keeping score solely by death rate, that doctor may get a failing grade even though he is excellent. Similarly, are the people who volunteer to play Dictator more cooperative than average?

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You didn’t really want to keep all that money, did you? You may not like this particular professor; you might even actively dislike him—but no one wants to look cheap in front of somebody else. What the heck, you decide, I’ll give away a few of my dollars. But even a cockeyed optimist wouldn’t call that altruism. In addition to scrutiny and selection bias, there’s one more factor to consider. Human behavior is influenced by a dazzlingly complex set of incentives, social norms, framing references, and the lessons gleaned from past experience—in a word, context. We act as we do because, given the choices and incentives at play in a particular circumstance, it seems most productive to act that way.

Trading and Exchanges: Market Microstructure for Practitioners
by Larry Harris
Published 2 Jan 2003

In either event, you must be very careful that the sample selection bias does not affect your conclusions. The sample selection bias arises when some process selects the information that you see about some object. If the process does not randomly select the information that you see, you will see only selected aspects of the object and your impression of it will not be accurate. Decisions that you make based upon your information therefore very likely will be faulty. * * * ▶ Blind Men Describe an Elephant The story of the blind men who examine different parts of an elephant illustrates the sample selection bias: Each man feels only the trunk, leg, side, or tail, and each respectively concludes that the elephant is like a snake, a tree trunk, a wall, or a rope.

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This type of sample selection bias is called the survivorship bias. Some large mutual fund companies start many new mutual funds every year. They keep the ones that perform well and kill the ones that fail. In this way, they are able to create the winners that they need to market their funds. If you are unaware of this process, you may give too much significance to past returns. You may not realize that the fund which generated superior past performance came to your attention only because it was among the best-performing funds of a large group of funds. 22.6.2 Avoiding the Sample Selection Bias Sample selection biases may be responsible for more trading losses than any other cause.

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If pyramid scheme promoters are not too greedy, if their excess returns are not too large, if they can convince their clients to not withdraw their funds, and if they can somehow control the audits of their portfolios, pyramid schemes can go undetected for a very long time. An investment manager’s performance record therefore is no substitute for doing the due diligence that all prudent investors must undertake to ensure that they are not contributing to pyramid schemes. 22.6 THE SAMPLE SELECTION BIAS The two preceding sections demonstrate that statistical tests for managers generally do not produce useful information about their skill. The properties of the tests described there were derived by assuming that analysts would use the tests under ideal circumstances. In practice, statistical performance evaluations rarely are applied under ideal circumstances.

Market Sense and Nonsense
by Jack D. Schwager
Published 5 Oct 2012

A number of indexes now correct for this bias, so while this bias is significant if present, it has become less important. Selection bias. Hedge funds decide whether to report their numbers to databases. Insofar as better-performing funds will be more likely to report their numbers, the self-selection process will create an upward bias. However, in this instance there is an offsetting effect in that funds that do particularly well and close to new investment may decide to stop reporting their numbers to avoid inquiries from new investors. Although it is difficult to say how these two offsetting effects balance out, from the perspective of a new investor, selection bias also creates an upward bias, since the universe of potential investments does not include closed funds.

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Although there may still be a survivorship bias at the fund of funds level (that is, defunct fund of funds), the effect will be much more muted than for single funds because the difference between a defunct and an active fund of funds is much smaller. Selection bias. This bias is eliminated for single funds at the fund of funds level because a fund’s result will be reflected in fund of funds data even if it chooses not to report. At the fund of funds level, both positive and negative selection bias effects are far more moderate. The negative bias effect is probably negligible for a fund of funds because most funds of funds will continue to report their numbers even if closed, in order to aid the marketing of other fund of funds products under the same umbrella.

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See Minimum acceptable return (MAR) ratio Marcus, Michael Margin Margin calls Marginal production loss Market bubbles Market direction Market neutral fund Market overvaluation Market panics Market price delays and inventory model of Market price response Market pricing theory Market psychology Market risk Market sector convertible arbitrage hedge funds and CTA funds hidden risk long-only funds market dependency past and future correlation performance impact by strategy Market timing skill Market-based risk Maximum drawdown (MDD) Mean reversion Mean-reversion strategy Merger arbitrage funds Mergers, cyclical tendency Metrics Minimum acceptable return (MAR) ratio and Calmar ratio Mispricing Mocking Monetary policy Mortgage standards Mortgage-backed securities (MBSs) Mortgages Multifund portfolio, diversified Mutual fund managers, vs. hedge fund managers Mutual funds National Futures Association (NFA) Negative returns Negative Sharpe ratio, and volatility Net asset valuation (NAV) Net exposure New York Stock Exchange (NYSE) Newsletter recommendation NINJA loans Normal distribution Normally distributed returns Notional funding October 1987 market crash Offsetting positions Option ARM Option delta Option premium Option price, underlying market price Option timing Optionality Out-of-the-money options Outperformance Pairs trading Palm Palm IPO Palm/3 Com Past high-return strategies Past performance back-adjusted return measures evaluation of going forward with incomplete information visual performance evaluation Past returns about and causes of future performance hedge funds high and low return periods implications of investment insights market sector past highest return strategy relevance of sector selection select funds and sources of Past track records Performance-based fees Portfolio construction principles Portfolio fund risk Portfolio insurance Portfolio optimization past returns volatility as risk measure Portfolio optimization software Portfolio rebalancing about clarification effect of reason for test for Portfolio risks Portfolio volatility Price aberrations Price adjustment timing Price bubble Price change distribution The price in not always right dot-com mania Pets.com subprime investment Pricing models Prime broker Producer short covering Professional management Profit incentives Pro-forma statistics Pro-forma vs. actual results Program sales Prospect theory Puts Quantitative measures beta correlation monthly average return Ramp-up period underperformance Random selection Random trading Random walk process Randomness risk Rare events Rating agencies Rational behavior Redemption frequency notice penalties Redemption liquidity Relative velocity Renaissance Medallion fund Return periods, high and low long term investment S&P performance Return retracement ratio (RRR) Return/risk performance Return/risk ratios vs. return Returns comparison measures relative vs. absolute objective Reverse merger arbitrage Risk assessment of for best strategy and leverage measurement vs. failure to measure measures of perception of vs. volatility Risk assessment Risk aversion Risk evaluation Risk management Risk management discipline Risk measurement vs. no risk measurement Risk mismeasurement asset risk vs. failure to measure hidden risk hidden risk evaluation investment insights problem source value at risk (VaR) volatility as risk measure volatility vs. risk Risk reduction Risk types Risk-adjusted allocation Risk-adjusted return Risk/return metrics Risk/return ratios Rolling window return charts Rubin, Paul Rubinstein, Mark Rukeyser, Louis S&P 500, vs. financial newsletters S&P 500 index S&P returns study of Sasseville, Caroline Schwager Analytics Module SDR Sharpe ratio Sector approach Sector funds Sector past performance Securities and Exchange Commission (SEC) Select funds, past returns and Selection bias Semistrong efficiency Shakespearian monkey argument Sharpe ratio back-adjusted return measures vs. Gain-to-pain ratio negative and Sortino ratio and volatility Short bias equity hedge funds Short selling Short volatility risk Side pockets Simons, Jim Soros, George Sortino ratio and Sharpe ratio upward bias in Speculative buying Speculators Standard deviation and expected return maximum drawdown (MDD) Stark & Company Statistical arbitrage Stewart, Jon Stock index Stock market news Stock selection Stock-picking skills Strategy overcrowding Strategy periods Strike price Strong efficiency Subprime ARMs, and foreclosure Subprime bonds Subprime borrowers Subprime loans Subprime mortgage crisis Survivorship bias Systematic trend following Tail ratio Tail risk Tech bubble Technical analysis Termination bias “The Jones Nobody Keeps Up With” (Loomis) Thematic portfolios 3Com Time magazine Track records comparison pitfalls data relevance good past performance hidden risk length of portfolio managers strategy and portfolio changes strategy efficacy Tranches Transaction slipping Trend-following strategies Tulipmania Tversky, Amos Two-direction underwater curve (2DUC) Underwater loans Unexpected developments Visible risk Visual performance evaluation net asset valuation (NAV) charts rolling window return charts underwater curve and 2DUC charts Volatility about downside and upside and downside risk high high upside impact of implied increases in and negative Sharpe ratio as risk proxy Volatility funds Volatility-based estimates, and risk evaluation Wall Street Week Weak efficiency When Genius Failed (Lowenstein) Whipsaw losses Williams, Jared Worst-case loss estimate Worst-case outcomes Ziemba, William T.

The Little Black Book of Decision Making
by Michael Nicholas
Published 21 Jun 2017

As a result, we will very often be wrong while at the same time being completely oblivious of this fact. There are three ways in which our confirmation bias can distort even the best decision-making process: Selection bias: It biases choices about what evidence is relevant, restricting the information that gets selected for evaluation. Interpretation bias: It leads to a biased interpretation of the information that does get selected. Hindsight bias: It distorts our memory. To examine the selection bias, researchers from Ohio State University secretly recorded how long participants spent reading articles in an online forum.7 The articles offered opposing views on a range of subjects, and it was found that when participants agreed with the perspective they were reading about they spent, on average, 36% more time reading the article.

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To examine the selection bias, researchers from Ohio State University secretly recorded how long participants spent reading articles in an online forum.7 The articles offered opposing views on a range of subjects, and it was found that when participants agreed with the perspective they were reading about they spent, on average, 36% more time reading the article. This selection bias becomes more polarised as people become more committed to their beliefs. An analysis by Valdis Krebs of political book-buying patterns during the 2008 United States presidential election campaign provides a great example.8 In the five years that he had been analysing these patterns there had always been some books that were purchased by supporters of both parties. However, as polling day approached, the overlap became smaller and smaller until, by October 2008, for the first time in several years of research, Krebs found no overlap – not a single book that appealed to the mindsets of both sides!

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metacognition mind see also brain; unconscious mind mindfulness mindset positive shift in slowing down motivated reasoning motivation naïve realism NASA Challenger disaster necessity neocortex Netflix neuroplasticity objectivity openness bounded awareness Fosbury intellect intentional attention lack of meditation neurological challenge practice responsiveness order outside-in approach overconfidence paranoia parenting Pascal, Blaise Pavlov, Ivan perception see also reality, perception of phobias practice predictability prefrontal cortex problem-solving procrastination purpose rationality reactivity brain processes fight-or-flight mechanism habits intentional attention stress threat response reality, perception of see also interpretation relaxation Relman, Arnold Seymour reptilian brain responsiveness intentional attention mindfulness threat response risk taking Ross, Lee rules Schwab, Klaus science scientific management seeking to disprove selection bias self-awareness adaptive change fear feedback intentional attention interpretation lack of mindfulness practice prefrontal cortex seeking to disprove self-deception Seligman, Martin Shakespeare, William Shaw, George Bernard simple systems skills, hard and soft slowing down smoking startle reflex Stoner, Jesse Lyn strategic planning stress beta waves breathing intentional attention mindlessness as response to fear subconscious see also unconscious mind success syndrome superstition System 1 and System Taylor, Frederick Winslow technical change technology “10 000 hour rule” tenacity Fosbury intellect inventors practice Tetlock, Philip thinking threat response time management Torvill, Jane trial and error Triune Brain Trump, Donald truth Tversky, Amos Twain, Mark uncertainty embracing fear of unconscious mind “unknown unknowns” Velcro Vipassana meditation visual cortex volatility Weaver, Warren Westen, Drew Wilkinson, Jonny Yerkes-Dodson Law of Arousal WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley’s ebook EULA.

The Big Fat Surprise: Why Butter, Meat and Cheese Belong in a Healthy Diet
by Nina Teicholz
Published 12 May 2014

When I mentioned all this to Stamler, he didn’t remember any part of this cancer-cholesterol debate. In this way, he is a microcosm of a larger phenomenon that allowed the diet-heart hypothesis to move forward: inconvenient results were consistently ignored; here again, “selection bias” was at work. An Extreme Case of Selection Bias There has been a lot of selective reporting and ignoring of the methodological problems over the years. But probably the most astonishing example of selection bias was the near-complete suppression of the Minnesota Coronary Survey, which was an outgrowth of the National Diet Heart Study. Also funded by NIH, the Minnesota Coronary Survey is the largest-ever clinical trial of the diet-heart hypothesis and therefore certainly belongs on the list along with Oslo, the Finnish Mental Hospital Study, and the LA Veterans Trial, but it is rarely included, undoubtedly because it didn’t turn out the way nutrition experts had hoped.

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Or, as one of the great science philosophers of the twentieth century, Karl Popper, described, “The method of science is the method of bold conjectures and ingenious and severe attempts to refute them.”V In seeing how these early studies from Roseto, Pennsylvania to North Dakota were overlooked or dismissed out of hand, it’s hard, as a student of the history of the diet-heart hypothesis, not to conclude that selection bias has consistently been practiced for decades. Dozens of trials either were forgotten or had their findings distorted. The ones we have reviewed here were early and relatively small. As we’ll see, the studies ignored or willfully misinterpreted later on were some of the biggest and most ambitious trials of diet and disease ever undertaken in the history of nutrition science. Alternative Ideas and the Opposition One of the hallmarks of selection bias is that people—even scientists trained to look for it—often don’t realize that they, themselves, might be suffering from it.

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In this process, it’s hard to overcome the essentially human instinct to select only those observations that conveniently support one’s own hypothesis while rejecting those that do not. A large number of psychological studies have shown that people respond to scientific or technical evidence in ways that justify their preexisting beliefs. “Selection bias,” as it’s called, is the danger of becoming overly attached to one’s own hypothesis or belief system. Resisting these “idols of the mind,” as the great seventeenth-century theorist Francis Bacon dubbed them, is exactly what the scientific method tries to do. A scientist must always try to disprove his or her own hypothesis.

Dataclysm: Who We Are (When We Think No One's Looking)
by Christian Rudder
Published 8 Sep 2014

As the Internet has democratized journalism, photography, pornography, charity, comedy, and so many other courses of personal endeavor, it will, I hope, eventually democratize our fundamental narrative. The sound is inchoate now, unrefined. But I’m writing this book to bring out what faint patterns I, and others, detect. This is the echo of the approaching train in ears pressed to the rail. Data science is far from perfect—there’s selection bias and many other shortcomings to understand, acknowledge, and work around. But the distance between what could be and what is grows shorter every day, and that final convergence is the day I’m writing to. I know there are a lot of people making big claims about data, and I’m not here to say it will change the course of history—certainly not like internal combustion did, or steel—but it will, I believe, change what history is.

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In this note, as everywhere in this chapter, “gay” and “bisexual” users are counted separately, and this calculation does not include the latter. 5 There are gay hookup apps specifically for casual sex: Grindr and Scruff are the best known services for men. The straight analogue for these apps is Tinder. It’s proportionately as popular, perhaps more so. Therefore, I don’t think selection bias (for long-term relationships) in OkCupid’s gay population is any worse than in its straight population, though I do admit this is an impossible thing to know for sure. 6 Forty-nine percent of straight men and gay women have reported four or fewer partners. 12. Know Your Place When I was in junior high we had a long lunch period, and since everyone was too grown up at that age to really play or enjoy themselves, after the eating was over, we all just posted up outside the school and waited for the bell to ring us back to class.

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This data above does not prove that the Mountain Time Zone is one big high-plains makeout party. In fact, the explanation is rather banal: if you are looking for people to have sex with in a place like Pierre, South Dakota, your local options are limited. So you try a dating site to find what you want. It’s simple selection bias in our data, but there’s meaning there: where people can’t find satisfaction in person, they create alternative digital communities. On a dating site, that means communities with similar sexual interests. On other sites with more diverse aims, where the users aren’t just there to flirt in groups of two (and occasionally three), you get something richer.

Beyond the Random Walk: A Guide to Stock Market Anomalies and Low Risk Investing
by Vijay Singal
Published 15 Jun 2004

The small sample bias is especially relevant to anomalies that do not have a reasonable explanation, especially if it appears that the mispricing has occurred just by chance. SELECTION BIAS Another bias that may creep into the discovery of mispricings is selection bias, that is, the sample may be biased in favor of finding the desired result. Assume you want to measure the ownership of cell phones in the general American population. If you polled only people working in Manhattan, your estimate will be biased upward because the sample is biased and the result is falsely attributed to the entire American population, including rural and less urban areas. In the case of stock market studies, a selection bias can creep in when the results arise from a certain part of the sample but seem to be representative of the entire market.

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. • Markets cannot be fully efficient because of the cost of collecting and analyzing information, cost of trading, and limits on the capital available to arbitrageurs. • All anomalies must be viewed with caution and skepticism, as spurious mispricings can surface for a variety of reasons, such as errors in defining normal return, data mining, survivorship bias, small sample bias, selection bias, nonsynchronous trading, and misestimation of risk. • Though anomalies should disappear in an efficient market, they may persist because they are not well understood, arbitrage is too costly, the profit potential is insufficient, trading restrictions exist, and behavioral biases exist. • Documented and valid anomalies may still be unprofitable because the evidence is based on averages (and may include a large fraction of losers), conditions responsible for the anomaly may change, and trading by informed investors may cause the anomaly to disappear. 

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Working paper, Department of Finance, Wharton School, University of Pennsylvania. Bos, Roger. 2000. Quantifying the Effect of Being Added to an S&P Index. Standard and Poor’s report, September. 193 194 Beyond the Random Walk Chan, Louis K. C., Narasimhan Jegadeesh, and Josef Lakonishok. 1995. Evaluating the Performance of Value Versus Glamour Stocks: The Impact of Selection Bias. Journal of Financial Economics 38(3), 269–96. Chen, Honghui, Greg Noronha, and Vijay Singal. 2004. The Price Response to S&P 500 Index Additions and Deletions: Evidence of Asymmetry and a New Explanation. Forthcoming in the Journal of Finance. Chordia, Tarun. 2001. Liquidity and Returns: The Impact of Inclusion into the S&P 500 Index.

Expected Returns: An Investor's Guide to Harvesting Market Rewards
by Antti Ilmanen
Published 4 Apr 2011

The comprehensive global studies by Dimson, Marsh, and Staunton address these questions by analyzing the performance of equity markets in 19 countries since 1900. Table 8.2 shows that the (market cap or GDP-weighted) global equity premium is about 0.5% to 0.8% lower than the U.S. premium for 1900–2009—consistent with mild selection bias. The authors argue that use of their world premium captures the bulk of any survivorship or selection bias given that the 19 countries in their database may have accounted for 90% of world equity market capitalization at the start of the 20th century [3]. Equity returns are a tad lower outside the U.S. but the broad patterns are similar. Table 8.2.

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Data mining is the best known, but I also mention peso problems, learning, market frictions, and the changing world. I call all of these explanations mirages because whatever predictability was observed in the past was sample specific and/or has no investment value for the future. Here is some color on each explanation. Data mining (also called data snooping, overfitting, or selection bias) Any interesting empirical regularities could be spurious (which does not mean that they all are). When thousands of academics and practitioners study the same historical dataset, all motivated to find profitable strategies, they (we) are bound to uncover many empirical relations that just happened to work well in sample and fail miserably out of sample.

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Given this option, funds with superior histories are more likely to report them. It is also natural that among many incubated funds the more successful ones will eventually be reported while poorer performers will not. Empirically, it is clear that average returns are lower if backfill filters are used. • Selection bias. Funds mainly report for marketing reasons. They may opt out not only because of poor performance but also because an established fund is closed and has no need to attract new capital. Because many large funds do not report to databases and are among the most successful, the sign of the bias, net of these two effects, is ambiguous

The Tyranny of Metrics
by Jerry Z. Muller
Published 23 Jan 2018

The patients who the surgeons declined to operate on because they were more high-risk—and hence would bring down the surgeon’s score—were not included in the metrics. Some of these sicker patients were referred to the Cleveland Clinic, and so the outcomes of their procedures did not show up in the New York metrics. As a result of this “case selection bias” (that is, creaming) some sicker patients were simply not operated on. Nor is it clear that the improvement in postoperative outcomes in New York State was a result of the publication of the metrics. It turns out that the same improvement occurred in the neighboring state of Massachusetts, where there was no public reporting of data.25 The phenomenon of risk-aversion means that some patients whose lives might be saved by a risky operation are simply never operated upon.

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INDEX abstract and formulaic knowledge, 59–60 “Academic Ranking of World Universities,” 75–76 accountability, 3–6; advocates of, 17–18, 113; growth in applications of, 63–64; quest for numerical metrics of, 40 Acemoglu, Daron, 72 achievement gap, 20, 91, 96–99 Adelphia, 144 Affordable Care Act, 104, 114–15 Afghanistan War, 131–34 agency capitalism, 148 American Recovery and Reinvestment Act, 94 Annals of Internal Medicine, 115–16 Arnold, Matthew, 12, 30–31, 92 authority, suspicion of, 41 Autor, David, 72 Baumol, William, 44 Bell, Daniel, 33 benchmarks, 6 Benghazi investigations, 162 Berwick, Donald, 119–20, 172 Bin Laden, Osama, 171 BlackRock, 149 Blair, Tony, 114 Bodies, 2–3 bounded rationality, 45 Bowen, William G., 44 Bratton, William J., 126 Bresch, Heather, 141, 142 Burns, Ed, 1, 129 Bush, George W., 11, 64, 89, 90 business and finance: financial crisis of 2008 and, 145–47; other dysfunctions in, 150–51; short-termism in, 147–50; when paying for performance works, and when it doesn’t, in, 137–45 business schools, 12–13, 138–39 Cable, Dan, 138 Campbell, Donald T., 19, 127 Campbell’s Law, 19, 24, 80, 93, 127 capitalism, 87, 172; agency, 148 case selection bias, 117–18. See also creaming Centers for Medicare and Medicaid, 112, 119 cheating, 24 Chronicle of Higher Education, The, 76 Circle, The, 140 civil rights law, 42 Cleveland Clinic, 107–8, 110–11, 117 Clinton, Bill, 64, 90 Clinton, Hillary, 162 Coleman Report, 98 colleges and universities.

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See metric fixation Forbes, 76 Ford Motor Company, 34 foreign aid and philanthropy, 153–56 Freedom of Information Act, 162 From Higher Aims to Hired Hands: The Social Transformation of American Business Schools and the Unfulfilled Promise of Management as a Profession, 12 gaming the metrics, 3, 24–25, 149–50 Geisinger Health System, 108–9, 110–11, 123 General Motors, 33 Geographical Information Systems (GIS), 126 Gibbons, Robert, 55–56 goals: displacement of, through diversion of effort, 169–70; value of short-term over long-term, 20 Goodhart’s Law, 19–20, 24 Google Ngram, 40, 159 Google Scholar, 79 Government Accountability Office, 156 Guardian, The, 163 Halbertal, Moshe, 160, 164 Hayek, Friedrich, 12, 59, 60–61 Healthgrades, 115 Henderson, Rebecca, 150 higher education, 9–14, 175–76; designed to make money, 86–87; encouraging everyone to pursue, 67–68; grading institutions in, 81–86; higher metrics through lower standards in, 69–73; measuring academic productivity, 78–80; pressure to measure performance in, 73–75; raising the number of winners lowering the value of winning with, 68–69; rankings, 75–78; value and limits of rankings in, 81 high-stakes testing, 93 Holmstrom, Bengt, 52, 169 Howard, Philip K., 41 human capital, 72, 98 impact factor measurement, 79 Improving America’s Schools Act, 90 information, distortion of, 23–24 innovation, 20; discouragement of, 140, 150–51, 171–72; employees moving to organizations that encourage, 173; unmeasurable risk for potential benefits of, 61–62 Institute of Medicine, 118–19 intimacy, 160 intrinsic rewards, 53–57, 119–20 Iraq War, 131–34 Johns Hopkins University, 109–10 Johnson, Lyndon, 98 Joint Commission, 115 Joint Stock Companies Act, 30 judgment, 6–7; distrust of, 39–42; measurement demanding, 176–77 “juking the stats,” 2 Kedourie, Elie, 62–63, 73 Kelvin, Lord, 17 Kennedy, Edward, 90 Keystone project, 109–10, 111–12, 176 Khurana, Rakesh, 12 Kilcullen, David, 131–34 Kiplinger, 76 Klarman, Seth, 47 Knight, Frank, 61–62, 151 knowledge: forms of, 59–60; practical, local, 62; pretense of, 60 Kohn, Alfie, 62 Kolberg, William, 90 Kozlowski, Dennis, 144 Lancelot, William, 33 leadership and organizational complexity, 44–47 Lehman Brothers, 146–47 Levy, Steven, 47 Limited Liability Act, 30 litigation, fear of, 42 London Business School, 138–39 Lowe, Robert, 29–30 Lumina Foundation, 67–68, 71 Luttwak, Edward, 35–37 luxury goods, 104 managerialism, 34–37 Manning, Bradley (later Chelsea),162–63 Mass Flourishing: How Grassroots Innovation Created Jobs, Challenge, and Change, 172 Masters of Management, 13 materialist bias, 36 Mayer-Schönberger, Viktor, 35 McNamara, Robert, 34–37, 131 measurement and improvement, 16–17, 101, 107, 111, 119, 123, 132, 176, 183 measuring inputs rather than outcomes, 23–24 “Measuring Progress in Afghanistan,” 132 measuring the most easily measurable, 23 measuring the simple when the desired outcome is complex, 23 Medicaid, 104 Medicare, 104, 114–16, 120–23 medicine: broader picture on metrics, pay-for-performance, rankings, and report cards in, 112–20; case selection bias in, 117–18; Cleveland Clinic, 107–8, 110–11; conclusions from success in, 110–12; cost disease and, 44; discouraging cooperation and common purpose in, 172; financial push to control costs in, 103–4, 119–20; Geisinger Health System, 108–9, 110–11, 123; Keystone project, 109–10, 111–12, 176; measured performance metrics in, 2–5, 107, 123, 176; ranking the American system of, 105–7; reducing readmissions test case, 120–23; rise of metric fixation with increased critique of, 42–43; tales of success in, 107–10 Mercurio, Jed, 2–3 Merton, Robert K., 12, 170 metric fixation, 4–9, 13; in business and finance, 137–51; cost disease and, 44; critique of the professions and apotheosis of choice in, 42–44; defined, 18; distortion of information with, 23–24; distrust of judgment leading to, 39–42; in higher education, 9–14, 67–87, 175–76; innovation and creativity stifled by, 20; key components of, 18; leadership and organizational complexity and, 44–47; lure of electronic spreadsheets in, 47; managerialism and, 34–37; in medicine, 2–5, 42–44, 103–23, 172, 176; by the military, 35–37, 131–35, 176; negative transformations of nature of work with, 19; pay for performance and, 19; in philanthropy and foreign aid, 153–56; in policing, 125–29, 175; predicting and avoiding negative consequences of, 169–73; recurrent flaws in, 23–25; relationship between measurement and improvement in, 17–19; in schools, 11, 24, 89, 175–76; Taylorism and, 31–34; theory of motivation and, 19–20; and transparency as enemy of performance, 159–65 metrics: checklist for when and how to use, 175–83; corruption or goal diversion in gathering and using, 182; costs of acquiring, 180; development of measures for, 181; diagnostic, 92–93, 103, 110, 123, 126, 176; diminishing utility of, 170; gaming the, 3, 23–24, 149–50; kind of information measured by, 177; media depictions of, 1–4; philosophical critiques of, 59–64; purposes of specific measurements and, 178–79; reasons leaders ask for, 180–81; recognition that not all problems are solvable by, 182–83; transactional costs of, 170; used to replace judgment, 6–7; usefulness of information from, 177–78 Michigan Keystone ICU Project, 109–10, 111–12, 176 Middle States Commission on Higher Education, 10–11 Milgrom, Paul, 52, 169 military, American, 35–37, 131–35, 176 Minsky, Hyman, 148 Mintzberg, Henry, 52 Mitchell, Ted, 82 Moneyball, 7 Morieux, Yves, 45, 170 mortgage backed securities, 146–47 motivation: extrinsic and intrinsic rewards and, 53–57, 119–20, 137–38, 144; theory of, 19–20 Muller, Jerry Z., 79 Mylan, 140–42, 143 National Alliance of Business, 90 National Assessment of Educational Progress (NAEP), 91, 97, 99 National Center for Educational Statistics, 97 National Center on Performance Incentives, 95–96 National Health Service, 104, 114, 116–17 National Security Agency, 163 Natsios, Andrew, 155–56 New Public Management, 51–53 Newsweek, 76 No Child Left Behind Act of 2001, 11, 24, 89, 100; problem addressed by, 89–91, 96; Race to the Top after, 94–95; unintended consequences of, 92–94.

Statistics in a Nutshell
by Sarah Boslaugh
Published 10 Nov 2012

The sample needs to be a good representation of the study population (the population to which the results are meant to apply) for the researcher to be comfortable using the results from the sample to describe the population. If the sample is biased, meaning it is not representative of the study population, conclusions drawn from the study sample might not apply to the study population. Selection bias exists if some potential subjects are more likely than others to be selected for the study sample. This term is usually reserved for bias that occurs due to the process of sampling. For instance, telephone surveys conducted using numbers from published directories by design remove from the pool of potential respondents people with unpublished numbers or those who have changed phone numbers since the directory was published.

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A manager is concerned about the health of his employees, so he institutes a series of lunchtime lectures on topics such as healthy eating, the importance of exercise, and the deleterious health effects of smoking and drinking. He conducts an anonymous survey (using a paper-and-pencil questionnaire) of employees before and after the lecture series and finds that the series has been effective in increasing healthy behaviors and decreasing unhealthy behaviors. Solution Selection bias and nonresponse bias, both of which affect the quality of the sample analyzed. The reported average annual salary is probably an overestimate of the true value because subscribers to the alumni magazine were probably among the more successful graduates, and people who felt embarrassed about their low salary were less likely to respond.

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However, because quota sampling is a nonprobability sampling method, you still have no way of knowing whether the people in the sample are representative of the population of interest. You might have an even representation of men and women in a quota sample, for instance, but are those in the sample representative of all the men and women who shop at the mall, let alone who live in the area? Quota sampling can also be subject to a particular type of selection bias, which is also a risk in convenience sampling. The data collector might approach people who seem most like himself (for instance in age) or who seem the friendliest or most approachable, rendering the sample even less useful as a means to acquire information about a larger population. Probability Sampling In probability sampling, every member of the population has a known probability to be selected for the sample.

The Data Detective: Ten Easy Rules to Make Sense of Statistics
by Tim Harford
Published 2 Feb 2021

See also choice research Bell, Vanessa, 256–57 Bem, Daryl, 111, 113–14, 119–23 benefits of statistical analysis, 9 Berti, Gasparo, 172 Bevacqua, Graciela, 194–95, 212 Beyth, Ruth, 248–49, 251, 254 biases biased assimilation, 35–36 confirmation bias, 33 current offense bias, 169 and motivated reasoning, 27–29, 32–36, 38, 131, 268 negativity bias, 95–99 non-response bias, 146–47 novelty bias, 95–99, 113, 114, 122 optimism bias, 96 and power of doubt, 13 publication bias, 113–16, 118–23, 125–27 racial bias in criminal justice, 176–79 in sampling, 135–38, 142–45, 147–51 selection bias, 2, 245–46 survivorship bias, 109–10, 112–13, 122–26 systematic bias in algorithms, 166 and value of statistical knowledge, 17 big data and certification of researchers, 182 and criminal justice, 176–79 and excessive credulity in data, 164–67 and found data, 149, 151, 152, 154 and Google Flu Trends, 153–57 historical perspective on, 171–75 influence in today’s world, 183 limitations and misuse of, 159–63, 170–71 proliferation of, 157–59 and teacher evaluations, 163–64 See also algorithms Big Data (Cukier and Mayer-Schönberger), 148, 157 “Big Duck” graphics, 216–18, 217, 229–30 Big Issue, The, 226n “Billion Pound-O-Gram, The” (infographic), 223 billionaires, 78–80 binge drinking, 75 Bird, Sheila, 68 bird’s-eye view of data, 61–64, 203, 221, 265 BizzFit, 108 Black Swan, The (Taleb), 101 Blastland, Michael, 10, 68, 93 blogs, 76 Bloomberg TV, 89 body count metrics, 58 Boijmans Museum, 20 Boon, Gerard, 19, 30–31 border wall debate, 93–94 Borges, Jorge Luis, 118 Boyle, Robert, 172–75 brain physiology, 270 Bredius, Abraham, 19–23, 29–32, 35, 43–45, 78, 242, 262 Bretton Woods conference, 262 Brettschneider, Brian, 224 Brexit, 71, 277 British Army, 213–14, 220–21 British Election Study, 145–46 British Medical Journal, 6, 67 British Treasury, 256–57 Broward County Sheriff’s Office, 176 Brown, Derren, 115 Brown, Zack “Danger,” 108 Buchanan, Larry, 229, 232 budget deficits, 188, 192–93, 195 Buffett, Warren, 259 Bureau of Economic Analysis, 190, 205 Bureau of Labor Statistics, 190, 205, 212 business-cycle forecasting, 258–59 business writing, 123–24 Butoyi, Imelda, 62–63 Cairo, Alberto, 227 Cambridge Analytica, 158 Cambridge University, 162.

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See also King’s College, Cambridge Cameron, David, 146 camouflage, 218–19 campaign finance, 274–75 Campbell, Donald T., 59 Campbell Collaboration, 134 Canada, 196–97, 209, 211 cancer diagnoses and research, 3–6, 24, 97, 241, 248, 279 Capital in the Twenty-First Century (Piketty), 83 capital punishment, 35–36 Caravaggio, Michelangelo, 30–31 carbon emissions, 272–73 Carter, Jimmy, 188 casualty statistics, 58, 231–37, 234, 235 categorization of statistical data, 68–70 causation vs. correlation, 15, 64, 156–57, 275 Census Bureau, 190, 205 census data, 147–48, 196–99, 205–6 Centers for Disease Control and Prevention (CDC), 153, 154, 155 Centre for Evidence-Based Medicine, 128 certification of researchers, 182 Chalmers, Iain, 133 Chambers, David, 259 charts, 215–16, 218n, 224, 227–29, 232–36, 232, 234, 235. See also visualization of data cheating, 164 cherry-picking data, 245–46. See also selection bias Chicago Tribune, 170–71 child abuse and neglect, 170–71 child benefits, 142 child safety, 170–71 childbirth data, 55 childhood vaccination, 53, 99 China, 59–61, 92 choice research, 105–8, 110–14, 123–24 Christ at Emmaus (forged painting), 19–22, 23, 30–32, 43 Christ in the House of Martha and Mary (Vermeer), 29–30 Christianity Is Evil (pamphlet), 248 cigarette smoking, 3–6, 14–15, 39, 52, 279 climate science and data and motivated reasoning, 34–35, 36–39, 268–70 and novelty bias in reporting, 100 and power of doubt, 13 and scale of news stories, 90 and visualization of data, 224 clinical research, 57, 120, 128, 139–40, 180–81.

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See also perspective on statistical data convergence of estimates, 246 conversion of units, 165n Corbett-Davies, Sam, 177 Corbyn, Jeremy, 14 coronavirus pandemic (COVID-19) and biases in journalism, 99, 102 and evaluation of statistical claims, 26, 29, 68, 68n and exponential growth, 41n and gender disparities, 140 and Nightingale Hospital, 213 and sanitary practices, 226n and selection bias in data, 109–10 statistical challenges of, 6–11, 120n and vaccination trends, 99 correlation vs. causation, 15, 64, 156–57, 275 corruption, 59, 209–10 cost-benefit analyses, 198, 199 Cotgreave, Andy, 232 COVID-19. See coronavirus pandemic (COVID-19) Cowperthwaite, John, 200–201, 203, 204 Crawford, Kate, 150 credibility of data, 192, 195 Credit Suisse, 80, 80n credulity in data, 164–67 Criado Perez, Caroline, 139 crime and criminal justice and alternative sanctions, 176–79 and bail recommendations, 158, 169, 180 and capital punishment, 35–36 and COMPAS system, 176–79 and human judgment vs. algorithms, 168–71 and missing data issues, 143 and murder rate data, 55, 87–89, 88n Crime Survey of England and Wales, 143 Crimean War, 213–14, 220, 226, 233–37 Cuddy, Amy, 121, 122 Cukier, Kenneth, 157 cultural influences, 37, 136–37, 201 curiosity, 265–79, 276n currency speculation, 255, 257–58 current offense bias, 169 curse of knowledge, 71–72 cyberspace, 149 cynicism and skepticism about statistics, 8–10, 13, 14, 266 Daily Mail, 133 dark data, 146 data recording practices, 66–67 dazzle camouflage, 218–19 De Meyer, Kris, 38–39 De Waarheid, 44 death penalty, 35–36 death rates, 214–15, 236.

A Manual for Creating Atheists
by Peter Boghossian
Published 1 Nov 2013

Quantum mechanics A branch of physics that provides a mathematical description of the behavior and interaction of energy and matter at the atomic and subatomic level. Relativism Either there is no absolute truth or it’s not possible to know the absolute truth. Scientific method A process by which one can make an objective investigation. Selection bias “Selection bias comes in two flavors: (1) self-selection of individuals to participate in an activity or survey, or as a subject in an experimental study; (2) selection of samples or studies by researchers to support a particular hypothesis.” The Skeptics Dictionary: http://skepdic.com/selectionbias.html Shermer, Michael (1954–) The founding publisher of Skeptic magazine, the executive director of the Skeptics Society, a monthly columnist for Scientific American, the host of the Skeptics Society, and bestselling author.

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I took it and read it. She was a Doctor of Naturopathic Medicine.) PB: No. I haven’t tried it because it doesn’t work. (I held the card.) RC: Oh, it works all right. I know it works. PB: Really? How do you know it works? RC: Because I’ve cured people of illnesses. I’ve seen it work. PB: Do you think selection bias has anything to do with that? RC: No. PB: What illnesses have you cured? RC: Everything. You name it, I’ve cured it. PB: Parkinson’s, Ebola, autism? RC: I’ve never treated anyone with those. PB: But if someone came in with one of those illnesses, could you cure them? RC: I don’t know. I could try.

The Shame Machine: Who Profits in the New Age of Humiliation
by Cathy O'Neil
Published 15 Mar 2022

Like me, when I desperately hacked my parents’ scale, most people would rather hide their setbacks. Shame muffles their testimony, muddying the statistics. This results in what statisticians call “selection bias.” It skews industry data toward success stories, almost all of them short-term. In the 2011 Weight Watchers study, more people dropped out of the Weight Watchers group than the control group. This selection bias is yet another reason to be skeptical of dieting analyses. Noom, a weight-loss program offering behavior modification, provides a prime example of marketing with sketchy statistics. The company targets upscale dieters, appealing to them in part through sponsorship plugs on National Public Radio.

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The analysis included 35,921 participants, all of whom installed the app and recorded their data two or more times a month for six consecutive months. How many other users signed up and never came back, or came back for three or four months—enough time to lose faith in the program? Those people weren’t counted. In fact, Noom’s decision to track only very active users is guaranteed to weed out people who have been overcome with shame. Selection bias, check. What’s more, Noom rests its case on results gathered over the course of a single year, far too short a time frame. As the 2008 Weight Watchers study demonstrated, dieters who register dramatic weight loss in the first year are all too likely to gain it back in years two through five.

Finding Alphas: A Quantitative Approach to Building Trading Strategies
by Igor Tulchinsky
Published 30 Sep 2019

The aggregated performance of alphas made from the same search space and search algorithm is called the batch performance. This is analogous to an orchestra, whose quality is measured based on the whole ensemble, not on the music of individual performers. Selection bias is a common pitfall, arising from an excessive focus on single alphas. After completing an automated search, it is tempting to test the out-of-sample performance of each output alpha and select only those that perform well. This practice, however, can introduce a selection bias into the alpha batch because the out-of-sample performance has been used in the alpha selection. As a result, it no longer can 118 Finding Alphas be considered out of sample.

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Shaw & Co. 8 design 25–30 automated searches 111–120 backtesting 33–41 case study 31–41 core concepts 3–6 data inputs 4, 25–26, 43–47 evaluation 28–29 expressions 4 flow chart 41 future performance 29–30 horizons 4–50 intraday alphas 219–221 machine learning 121–126 noise reduction 26 optimization 29–30 prediction frequency 27 quality 5 risk-on/risk off alphas 246–247 robustness 89–93 smoothing 54–55, 59–60 triple-axis plan 83–88 universe 26 value 27–30 digital filters 127–128 digitization 7–9 dimensionality 129–132 disclosures 192 distressed assets 202–203 diversification automated searches 118–119 exchange-traded funds 233 portfolios 83–88, 108 DL see deep learning dot (inner) product 63–64 Dow, Charles 7 DPIN see dynamic measure of the probability of informed trading drawdowns 106–107 dual timestamping 78 dynamic measure of the probability of informed trading (DPIN) 214–215 dynamic parameterization 132 early-exercise premium 174 earnings calls 181, 187–188 earnings estimates 184–185 earnings surprises 185–186 efficiency, automated searches 111–113 Index295 efficient markets hypothesis (EMH) 11, 135 ego 19 elegance of models 75 EMH see efficient markets hypothesis emotions 19 ensemble methods 124–125 ensemble performance 117–118 estimation of risk 102–106 historical 103–106 position-based 102–103 shrinkage 131 ETFs see exchange-traded funds Euclidean space 64–66 evaluation 13–14, 28–29 backtesting 13–14, 33–41, 69–76 bias 77–82 bootstrapping 107 correlation 28–29 cutting losses 20–21 data selection 74–75 drawdowns 107 information ratio 28 margin 28 overfitting 72–75 risk 101–110 robustness 89–93 turnover 49–60 see also validation event-driven strategies 195–205 business cycle 196 capital structure arbitrage 204–205 distressed assets 202–203 index-rebalancing arbitrage 203–204 mergers 196–199 spin-offs, split-offs & carve-outs 200–202 exchange-traded funds (ETFs) 223–240 average daily trading volume 239 challenges 239–240 merits 232–233 momentum alphas 235–237 opportunities 235–238 research 231–240 risks 233–235 seasonality 237–238 see also index alphas exit costs 19, 21 expectedness of news 164 exponential moving averages 54 expressions, simple 4 extreme alpha values 104 extrinsic risk 101, 106, 108–109 factor risk heterogeneity 234 factors financial statements 147 to alphas 148 failure modes 84 fair disclosures 192 fair value of futures 223 Fama–French three-factor model 96 familiarity bias 81 feature extraction 130–131 filters 127–128 finance blogs 181–182 finance portals 180–181, 192 financial statement analysis 141–154 balance sheets 143 basics 142 cash flow statements 144– 145, 150–152 corporate governance 146 factors 147–148 fundamental analysis 149–154 growth 145–146 income statements 144 negative factors 146–147 special considerations 147 finite impulse response (FIR) filters 127–128 296Index FIR filters see finite impulse response filters Fisher Transform 91 five-day reversion alpha 55–59 Float Boost 125 forecasting behavioral economics 11–12 computer adoption 7–9 frequencies 27 horizons 49–50 statistical arbitrage 10–11 UnRule 17–21 see also predictions formation of the industry 8–9 formulation bias 80 forward-looking bias 72 forwards 241–249 checklist 243–244 Commitments of Traders report 244–245 instrument groupings 242–243 seasonality 245–246 underlying assets 241–242 frequencies 27 full text analysis 164 fundamental analysis 149–154 future performance 29–30 futures 241–249 checklist 243–244 Commitments of Traders report 244–245 fair value 223 instrument groupings 242–243 seasonality 245–246 underlying assets 241–242 fuzzy logic 126 General Electric 200 generalized correlation 64–66 groupings, futures and forwards 242–243 group momentum 157–158 growth analysis 145–146 habits, successful 265–271 hard neutralization 108 headlines 164 hedge fund betas see risk factors hedge funds, initial 8–9 hedging 108–109 herding 81–82, 190–191 high-pass filters 128 historical risk measures 103–106 horizons 49–50 horizontal mergers 197 Huber loss function 129 humps 54 hypotheses 4 ideas 85–86 identity matrices 65 IIR filters see infinite impulse response filters illiquidity premium 208–211 implementation core concepts 12–13 triple-axis plan 86–88 inaccuracy of models 10–11 income statements 144 index alphas 223–240 index changes 225–228 new entrants 227–228 principles 223–225 value distortion 228–230 see also exchange-traded funds index-rebalancing arbitrage 203–204 industry formation 8–9 industry-specific factors 188–190 infinite impulse response (IIR) filters 127–128 information ratio (IR) 28, 35–36, 74–75 initial hedge funds 8–9 inner product see dot product inputs, for design 25–26 integer effect 138 intermediate variables 115 Index297 intraday data 207–216 expected returns 211–215 illiquidity premium 208–211 market microstructures 208 probability of informed trading 213–215 intraday trading 217–222 alpha design 219–221 liquidity 218–219 vs. daily trading 218–219 intrinsic risk 102–103, 105–106, 109 invariance 89 inverse exchange-traded funds 234 IR see information ratio iterative searches 115 Jensen’s alpha 3 L1 norm 128–129 L2 norm 128–129 latency 46–47, 128, 155–156 lead-lag effects 158 length of testing 75 Level 1/2 tick data 46 leverage 14–15 leveraged exchange-traded funds 234 limiting methods 92–93 liquidity effect 96 intraday data 208–211 intraday trading 218–219 and spreads 51 literature, as a data source 44 look-ahead bias 78–79 lookback days, WebSim 257–258 looking back see backtesting Lo’s hypothesis 97 losses cutting 17–21, 109 drawdowns 106–107 loss functions 128–129 low-pass filters 128 M&A see mergers and acquisitions MAC clause see material adverse change clause MACD see moving average convergence-divergence machine learning 121–126 deep learning 125–126 ensemble methods 124–125 fuzzy logic 126 look-ahead bias 79 neural networks 124 statistical models 123 supervised/unsupervised 122 support vector machines (SVM) 122, 123–124 macroeconomic correlations 153 manual searches, pre-automation 119 margin 28 market commentary sites 181–182 market effects index changes 225–228 see also price changes market microstructure 207–216 expected returns 211–215 illiquidity premium 208–211 probability of informed trading 213–215 types of 208 material adverse change (MAC) clause 198–199 max drawdown 35 max stock weight, WebSim 257 mean-reversion rule 70 mean-squared error minimization 11 media 159–167 academic research 160 categorization 163 expectedness 164 finance information 181–182, 192 momentum 165 novelty 161–162 298Index sentiment 160–161 social 165–166 mergers and acquisitions (M&A) 196–199 models backtesting 69–76 elegance 75 inaccuracy of 10–11 see also algorithms; design; evaluation; machine learning; optimization momentum alphas 155–158, 165, 235–237 momentum effect 96 momentum-reversion 136–137 morning sunshine 46 moving average convergencedivergence (MACD) 136 multiple hypothesistesting 13, 20–21 narrow framing 81 natural gas reserves 246 negative factors, financial statements 146–147 neocognitron models 126 neural networks (NNs) 124 neutralization 108 WebSim 257 newly indexed companies 227–228 news 159–167 academic research 160 categories 163 expectedness 164 finance information 181–182, 192 momentum 165 novelty 161–162 relevance 162 sentiment 160–161 volatility 164–165 NNs see neural networks noise automated searches 113 differentiation 72–75 reduction 26 nonlinear transformations 64–66 normal distribution, approximation to 91 novelty of news 161–162 open interest 177–178 opportunities 14–15 optimization 29–30 automated searches 112, 115–116 loss functions 128–129 of parameter 131–132 options 169–178 concepts 169 open interest 177–178 popularity 170 trading volume 174–177 volatility skew 171–173 volatility spread 174 option to stock volume ratio (O/S) 174–177 order-driven markets 208 ordering methods 90–92 O/S see option to stock volume ratio outliers 13, 54, 92–93 out-of-sample testing 13, 74 overfitting 72–75 data mining 79–80 reduction 74–75, 269–270 overnight-0 alphas 219–221 overnight-1 alphas 219 parameter minimization 75 parameter optimization 131–132 PCA see principal component analysis Pearson correlation coefficients 62–64, 90 peer pressure 156 percent profitable days 35 performance parameters 85–86 Index299 PH see probability of heuristicdriven trading PIN see probability of informed trading PnL see profit and loss pools see portfolios Popper, Karl 17 popularity of options 170 portfolios correlation 61–62, 66 diversification 83–88, 108 position-based risk measures 102–103 positive bias 190 predictions 4 frequency 27 horizons 49–50 see also forecasting price changes analyst reports 190 behavioral economics 11–12 efficient markets hypothesis 11 expressions 4 index changes 225–228 news effects 159–167 relative 12–13, 26 price targets 184 price-volume strategies 135–139 pride 19 principal component analysis (PCA) 130–131 probability of heuristic-driven trading (PH) 214 probability of informed trading (PIN) 213–215 profit and loss (PnL) correlation 61–62 drawdowns 106–107 see also losses profit per dollar traded 35 programming languages 12 psychological factors see behavioral economics put-call parity relation 174 Python 12 quality 5 quantiles approximation 91 quintile distributions 104–105 quote-driven markets 208 random forest algorithm 124–125 random walks 11 ranking 90 RBM see restricted Boltzmann machine real estate investment trusts (REITs) 227 recommendations by analysts 182–183 recurrent neural networks (RNNs) 125 reduction of dimensionality 130–131 of noise 26 of overfitting 74–75, 269–270 of risk 108–109 Reg FD see Regulation Fair Disclosure region, WebSim 256 regions 85–86 regression models 10–11 regression problems 121 regularization 129 Regulation Fair Disclosure (Reg FD) 192 REITs see real estate investment trusts relationship models 26 relative prices 12–13, 26 relevance, of news 162 Renaissance Technologies 8 research 7–15 analyst reports 179–193 automated searches 111–120 backtesting 13–14 300Index behavioral economics 11–12 computer adoption 7–9 evaluation 13–14 exchange-traded funds 231–240 implementation 12–13 intraday data 207–216 machine learning 121–126 opportunities 14–15 perspectives 7–15 statistical arbitrage 10–11 triple-axis plan 83–88 restricted Boltzmann machine (RBM) 125 Reuleaux triangle 70 reversion alphas, five-day 55–59 risk 101–110 arbitrage 196–199 control 108–109 drawdowns 106–107 estimation 102–106 extrinsic 101, 106, 108–109 intrinsic 102–103, 105–106, 109 risk factors 26, 95–100 risk-on/risk off alphas 246–247 risk-reward matrix 267–268 RNNs see recurrent neural networks robustness 89–93, 103–106 rules 17–18 evaluation 20–21 see also algorithms; UnRule Russell 2000 IWM fund 225–226 SAD see seasonal affective disorder scale of automated searches 111–113 search engines, analyst reports 180–181 search spaces, automated searches 114–116 seasonality exchange-traded funds 237–238 futures and forwards 245–246 momentum strategies 157 and sunshine 46 selection bias 77–79, 117–118 sell-side analysts 179–180 see also analyst reports sensitivity tests 119 sentiment analysis 160–161, 188 shareholder’s equity 151 Sharpe ratios 71, 73, 74–75, 221, 260 annualized 97 Shaw, David 8 shrinkage estimators 131 signals analysts report 190, 191–192 cutting losses 20–21 data sources 25–26 definition 73 earnings calls 187–188 expressions 4 noise reduction 26, 72–75 options trading volume 174–177 smoothing 54–55, 59–60 volatility skew 171–173 volatility spread 174 sign correlation 65 significance tests 119 Simons, James 8 simple moving averages 55 simulation backtesting 71–72 WebSim settings 256–258 see also backtesting size factor 96 smoothing 54–55, 59–60 social media 165–166 sources of data 25–26, 43–44, 74–75 automated searches 113–114 see also data sparse principal component analysis (sPCA) 131 Spearman’s rank correlation 90 Index301 special considerations, financial statements 147 spin-offs 200–202 split-offs 200–202 spreads and liquidity 51 and volatility 51–52 stat arb see statistical arbitrage statistical arbitrage (stat arb) 10–11, 69–70 statistical models, machine learning 123 step-by-step construction 5, 41 storage costs 247–248 storytelling 80 subjectivity 17 sunshine 46 supervised machine learning 122 support vector machines (SVM) 122, 123–124 systemic bias 77–80 TAP see triple-axis plan tax efficiency, exchange-traded funds 233 teams 270–271 temporal-based correlation 63–64, 65 theory-fitting 80 thought processes of analysts 186–187 tick data 46 timestamping and bias 78–79 tracking errors 233–234 trades cost of 50–52 crossing effect 52–53 latency 46–47 trend following 18 trimming 92 triple-axis plan (TAP) 83–88 concepts 83–86 implementation 86–88 tuning of turnover 59–60 see also smoothing turnover 49–60 backtesting 35 control 53–55, 59–60 costs 50–52 crossing 52–53 examples 55–59 horizons 49–50 smoothing 54–55, 59–60 WebSim 260 uncertainty 17–18 underlying principles 72–73 changes in 109 understanding data 46 unexpected news 164 universes 26, 85–86, 239–240, 256 UnRule 17–18, 20–21 unsupervised machine learning 122 validation, data 45–46 valuation methodologies 189 value of alphas 27–30 value distortion, indices 228–230 value factors 96 value investing 96, 141 variance and bias 129–130 vendors as a data source 44 vertical mergers 197 volatility and news 164–165 and spreads 51–52 volatility skew 171–173 volatility spread 174 volume of options trading 174–177 price-volume strategies 135–139 volume-synchronized probability of informed trading (VPIN) 215 302Index VPIN see volume-synchronized probability of informed trading weather effects 46 WebSim 253–261 analysis 258–260 backtesting 33–41 data types 255 example 260–261 settings 256–258 weekly goals 266–267 weighted moving averages 55 Winsorization 92–93 Yahoo finance 180 Z-scoring 92 

Our Kids: The American Dream in Crisis
by Robert D. Putnam
Published 10 Mar 2015

Harris, “Family Income, Parent Education, and Perceived Constraints as Predictors of Observed Program Quality and Parent Rated Program Quality,” Nebraska Center for Research on Children, Youth, Families and Schools (Lincoln, NE: CYFS, 2011). Methodologists are steadily improving measures of day care quality and methods for dealing with selection bias (mothers who choose higher-quality day care may be better mothers in other respects, too, so we can’t be sure it’s the day care that matters). The summary given in the text is our best judgment, given all the evidence available today. 65. Lisa Gennetian, Danielle Crosby, Chantelle Dowsett, and Aletha Huston, “Maternal Employment, Early Care Settings and the Achievement of Low-Income Children,” Next Generation Working Paper No. 30 (New York: MDRC, 2007). 66.

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While racial segregation continues to be a major national problem, virtually all relevant studies have concluded that class segregation is at least as pernicious in its effects on student achievement. See Richard D. Kahlenberg, “Socioeconomic School Integration,” North Carolina Law Review 85 (June 2007): 1545–94. 29. As with any discussion of contextual effects, this literature is fraught with methodological issues, especially selection bias. For example, since poor kids are not randomly assigned to schools, something about those who end up in high-income schools may predispose them to higher achievement, quite apart from the schools or their fellow students. Douglas Lee Lauen and S. Michael Gaddis, “Exposure to Classroom Poverty and Test Score Achievement: Contextual Effects or Selection?

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In our discussion of mentoring, “rich” and “poor” refer to the top and bottom quartiles of a composite measure of socioeconomic status. 28. Robert J. Sampson, Great American City: Chicago and the Enduring Neighborhood Effect (Chicago: University of Chicago Press, 2012), 356, emphasis in original. The study of neighborhood effects has been tormented by complicated methodological concerns, especially what is termed “selection bias.” Since people generally choose where to live, if people in a given neighborhood have distinctive characteristics, it is possible that they brought those traits with them to the neighborhood, rather than those traits being “caused” by the neighborhood context. The best contemporary studies, however, have been attuned to that risk, and our discussion here is based on findings that seem robust in the face of that methodological issue.

Commodity Trading Advisors: Risk, Performance Analysis, and Selection
by Greg N. Gregoriou , Vassilios Karavas , François-Serge Lhabitant and Fabrice Douglas Rouah
Published 23 Sep 2004

Both the time in existence and the year trading began had negative coefficients. The negative sign is at least partly due to selectivity bias. Some CTAs were added to the database after they began trading. CTAs with poor performance may not have provided data. This could cause CTAs to have higher returns in their first years of trading. A negative sign on the first-year variable suggests that the firms entering the database in more recent years have lower returns. Thus, selectivity bias may be less in more recent years. CTA returns also may genuinely erode over time. If CTAs do not change their trading system over time, others may discover the same inefficiency through their own testing.

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The adjustment factor of 100 is used since the data are measured as percentages. 1The 33 Performance of Managed Futures in previous literature. The conventional wisdom as to why CTAs have higher returns is that they incur lower costs. However, CTA returns may be higher because of selectivity or reporting biases. Selectivity bias is not a major concern here, because the comparison is among CTAs, not between CTAs and some other investment. Faff and Hallahan (2001) argue that survivorship bias is more likely to cause performance reversals than performance persistence. The data used show considerable kurtosis (see Table 3.1).

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CHARACTERISTICS OF CTA Before we engage in a detailed analysis of the risk-return properties of the CTA, a word of caution is necessary: Unlike traditional asset classes (bonds and equity), where performance data and benchmarks are readily and reliably available, the infrastructure and reliability of performance data for alternative investments, in general, and CTAs, in particular, are still rather underdeveloped. In this chapter, the CTA Qualified Universe index3 (CTA QU) is used to give an overall picture of CTA, as it is more representative of the performance of trading advisors as a whole and cannot be criticized as having selection bias. The sample portfolio is made up of CTA, Canadian, U.S., and international equities as well as domestic bonds. Canadian equities are represented by the Standard & Poor’s (S&P)/Toronto Stock Exchange index, the CTA by the CTA QU Index (from CISDM database), the U.S. equities asset by the S&P 500 Index, the international equities asset by the Morgan Stanley Capital Index for Europe, Asia, and the Far East (MSCI EAFE), and the bonds by the Scotia McLeod universe bond index.

Quantum Computing for Everyone
by Chris Bernhardt
Published 19 Mar 2019

Unfortunately, this property of not interacting readily with the outside world makes it difficult to measure them. In experiments involving photons, many of the entangled photons escape measurement, so it is theoretically possible that there is some selection bias going on—the results are reflecting the properties of a nonrepresentative sample. To counter the selection bias loophole, electrons have been used. But if electrons are used, how do you get the entangled electrons far enough apart before you measure them? This is exactly the problem that the team from Delft, which we mentioned in the previous chapter, solved using electrons trapped in diamonds that are entangled with photons.

Circus Maximus: The Economic Gamble Behind Hosting the Olympics and the World Cup
by Andrew Zimbalist
Published 13 Jan 2015

A subsequent study by Wolfgang Maennig and Felix Richter, however, effectively critiqued Rose and Spiegel on the grounds that their sample of countries was not representative: We challenge the empirical findings of Rose and Spiegel because they compare Olympic nations such as the United States, Japan, Germany, Canada, Italy, Spain, and Australia, which have been among the leading export nations for centuries, to all other nations. Their comparison of structurally different, nonmatching groups might suffer from a selection bias.18 When Maennig and Richter corrected for this bias by using only structurally similar countries, they found that the positive trade-signaling effect completely disappeared. Thus, we are left without any empirical evidence to confirm the touted benefit. Qualitative and Other Benefits The list of alleged qualitative benefits associated with hosting the Olympics is long and reaches in many directions: improved management practices and business culture, uplifted mood, reduced crime, higher real estate values, increased exercise and participation in sports, better sustainability practices, and improved values.

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S. Billings and J. Holladay, “Should Cities Go for the Gold? The Long-Term Impacts of Hosting the Olympics,” Economic Inquiry 50, no. 3 (2012): 754–72. 17. Wonho Song, using the Rose and Spiegel data set, found increases to exports and to tourism. However, these findings suffer from the same selection bias found in Rose and Spiegel. W. Song, “Impacts of Olympics on Exports and Tourism,” Journal of Economic Development 35, no. 4 (2010). 18. W. Maennig and F. Richter, “Exports and Olympic Games: Is There a Signal Effect?,” Journal of Sports Economics 13, no. 6 (2012): 636. 19. Robert Baumann, T.

The Singularity Is Nearer: When We Merge with AI
by Ray Kurzweil
Published 25 Jun 2024

In the memorable formulation of astronomer Hugh Ross, the likelihood of all this fine-tuning happening by chance is like “the possibility of a Boeing 747 aircraft being completely assembled as a result of a tornado striking a junkyard.”[75] The most common explanation of this apparent fine-tuning states that the very low probability of living in such a universe is explained by observer selection bias.[76] In other words, in order for us to even be considering this question, we must inhabit a fine-tuned universe—if it had been otherwise, we wouldn’t be conscious and able to reflect on that fact. This is known as the anthropic principle. Some scientists believe that such an explanation is adequate.

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As any novelist or screenwriter can tell you, capturing an audience’s interest usually requires an element of escalating danger or conflict.[20] From ancient mythology to Star Wars, this is the pattern that grabs our brains. As a result—sometimes deliberately and sometimes quite organically—the news tries to emulate this paradigm. Social media algorithms, which are optimized to maximize emotional response to drive user engagement and thus ad revenue, exacerbate this even further.[21] This creates a selection bias toward stories about looming crises while relegating the kinds of headlines cited at the beginning of this chapter to the bottom of our news feeds. Our attraction to bad news is in fact an evolutionary adaptation. Historically it’s been more important for our survival to pay attention to potential challenges.

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See also labor Normandie (ship), 113 North Dakota State University, 115 North Korea, 269, 274 nostalgia, 115–16 Nottingham lace, 198–99. See also Luddites nuclear treaties, 269 nuclear weapons, 268–70, 274 nucleus, 71, 261 nutrition, 192, 202, 255 O Oak Ridge National Laboratory, 61 observer selection bias, 98–99 occupational therapists, 198 On the Origin of Species (Darwin), 39 OpenAI. See also large language models ChatGPT, 52–53, 198 CLIP, 44 Codex, 50 DALL-E, 49–50 GPT-2, 47 GPT-3, 47–48, 49, 52, 55, 239, 324n GPT-3.5, 52, 55 GPT-4, 2, 9, 52–56, 65 optimism, 120, 121, 163, 233, 254, 270 orchestrated objective reduction (Orch OR), 330n Organisation for Economic Co-operation and Development (OECD), 138, 198 organ transplants, 186 origin of universe, 29–30, 95–96 Osborne, Michael, 197, 198, 219 outer misalignment, 278 Oxford University, 197–98 Future of Humanity Institute, 62, 268–69 oxidants, 258 oxygen, 69, 96, 178, 259, 263 P Pan Am, 113 pancreas, 259 pancreatic islet cells, 192 panprotopsychism, 80–82, 86, 88, 91, 94, 101, 105.

Economic Gangsters: Corruption, Violence, and the Poverty of Nations
by Raymond Fisman and Edward Miguel
Published 14 Apr 2008

Also, the very fact that we were able to conduct a detailed statistical analysis of Vietnam’s economic recovery makes it, almost by definition, a success story: collecting reliable household surveys and censuses of the type required for the studies of bombing in Vietnam and Japan is a luxury that the poorest nations can rarely afford. Countries with good data are almost never basket cases. This may lead to what economists call “selection bias”: countries where the economy and institutions have collapsed after wars (like Chad, Afghanistan, or Congo) lack reasonable data, so we don’t get to study the persistence of war’s negative effects, while we are able to study the success stories in greater detail. The postwar recoveries of Japan and Vietnam prove that rapid recovery is possible, but they could still be more the exception than the rule.

…

See specific countries and issues Agnelli, Giovanni, 49 Amassalik Inuit, 138 Amazon (company), 25 Angola, 96, 120b, 175; diamond mining and, 181b–85b; economic revival of, 184b antiparasitic drugs, school attendance and, 193–95 armed conflict, 148–55; Africa and, 114–16, 174–78; civil versus foreign, 173–74; disarmament and, 175–76; economic factors and, 116–17, 120–22; GDP and, 124; government stability and, 176–78; infrastructure investment and, 162–63, 170–71; OECD and, 120–21; political transformation and, 163–64; rainfall and, 122–27, 149; reconciliation and, 179–81; selection bias and, 174; technological inno- vation and, 164; tribal hatreds and, 116–17 Bakrie, Aburizal, 34, 38 behavioral economics, 96–97, 222n8 Bellow, Adam: In Praise of Nepotism, 30 Bimantara Citra, 33–40 Blood Diamond, 183b Bloomberg, Michael, 104 Bono, 9 Borsuk, Rick, 37–38 Botswana, 20–21; Drought Relief Program, 152–53, 199–200 bribery, commerce and, 66–67 Bush, George W., 32, 73–74, 174, 217n4 Busia (Kenya), 193–95, 232n9 Canada: corruption in, 95; United States and, 94–95 Capone, Al, 5–7 Chad, 17–18; corruption and, 156; economic decline of, 111–12; I N DEX Chad (continued) global warming and, 131; Lake Chad, 111–12; paperwork delays in, 66–67; petroleum deposits in, 155–58; political turmoil in, 112–13; rainfall and, 114; violence in, 175; World Bank and, 156–58 cheap talk, 18–20; violence and, 118b–19b Cheney, Dick, 29, 51–52 China: 1998 anticorruption campaign and, 70–73; global warming and, 127–29; smuggling and, 55–57; tariffs and, 60–64, 221n4, 221n6 China National Petroleum Company (CNPC), 185b Clodfelter, Michael, 160–61 coffee, 117–18, 149–50 Collier, Paul, 215n9, 228n20, 230n13 Colombia, 76–78, 102–3, 142 commodity prices, 117–18, 149–50, 227n15 conflict traps, Chad and, 113–14 containerization, 56–57 corruption: bottom line on, 102–3; cheap talk and, 18–20; culture and, 80–81, 87, 102–3; definition of, 18, 83, 216n12; economic growth and, 41–46; income level and, 91–92; mea sur ing, stock markets and, 24–29; national pride and, 100–102; outsiders and, 41–43; poverty and, 15–17; “Scramble for Africa” and, 101–2; stock markets and, 24–27; wages and, 189, 230n3.

Robots Will Steal Your Job, But That's OK: How to Survive the Economic Collapse and Be Happy
by Pistono, Federico
Published 14 Oct 2012

As we have seen, research shows that there is a correlation between income and general well-being (albeit fairly complicated and multifaceted), but it is unclear if there is a causation, and if so, which way does it go? We know that happier people are generally richer than the average, but we also know that happy people are less stressed, more sociable, more productive, and therefore more successful. So what is causing what exactly? The problem of reverse causation and selection bias is a serious one. People who are generally lonely and unhappy tend to be dismissed when looking for a job, they are more likely to become unemployed and to stay unemployed. Then there is another question. Would people be just as happy if they had the same income, but without having to work? Maybe it is not work itself that matters, but what it represents: Access.

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Many studies have found, in many countries and many time periods, that personally experiencing unemployment makes people very unhappy.154 In their ground-breaking study of Britain, Clark and Oswald summarise their result as follows: “joblessness depressed well-being more than any other single characteristic, including important negative ones such as divorce and separation”.155 Great Scott! More then divorce and separation? Is being employed such a powerful force in determining our general well-being? Apparently, it is. A while back we pondered about the possibility of reverse causation due to a selection bias in the income determination, could there be the same problem with employment? In other words, is unemployment causing unhappiness, or is it the other way round? Many studies with longitudinal data gathered before and after particular workers lost their jobs, suggest that there is evidence that unhappy people do indeed perform poorly on the labour market, but the main causation seems clearly to run from unemployment to unhappiness.156 Other studies in social psychology also come to similar conclusions.157 Let us stop for a moment and look at what we have discovered so far.

Finance and the Good Society
by Robert J. Shiller
Published 1 Jan 2012

This distinction must in part have to do with the fact that bankers typically stay out of the most volatile, headline-grabbing markets. But perhaps too it is because bankers, in contrast to hedge fund managers and the like, are following a long and time-honored tradition, extending back hundreds of years, which has evolved to solve certain problems—including liquidity, moral hazard and selection bias, and transaction service problems—to the satisfaction of most people most of the time. The anger toward bankers takes a very di erent form. It seems to be anger at their power and presumption, at their single-minded pursuit of money. And the anger ares up whenever there is a banking crisis and the governments of the world come to the rescue of these wealthy interests.

…

And even if they do make the occasional bad call, they have numerous other investments in their portfolios, a strategy that generally helps them maintain their integrity and reputations—except for the occasional severe financial crisis, during which, admittedly, some may fail or be bailed out. Banks solve yet another problem that less-skilled investors face: a selection bias problem. Those who are paying less attention to researching their investments will tend to be the more easily victimized; they will wind up with the “lemons” among investments because more skilled investors will snap up the better ones. Most individuals have no way of evaluating the trustworthiness of businesses in which they might invest.

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Hence banking plays an even bigger role in the economies of less-developed countries.5 In contrast, the role of traditional banks in the economies of more advanced countries has been in decline for decades: the fraction of these countries’ debt that is accounted for by traditional bank loans has been falling.6 This is so because the quality of publicly available information about securities is improving, and so the moral hazard and selection bias problems are reduced. Banks will increasingly be transformed into more complex institutions, but their traditional banking business will not go away entirely. Such banking meets too many of society’s needs, and banks’ public persona—current events notwithstanding—is too strong. The Evolution and Future of Banking Indeed the severe nancial crisis that began in 2007 was not due to any failures in the traditional banking business model, but instead to certain new kinds of business models, in which loans made to homeowners were not retained on the books of banks and other mortgage originators but bundled together into securities and sold o to other investors, including other banks—reintroducing the very problem of moral hazard that banks were supposed to solve.

Antifragile: Things That Gain From Disorder
by Nassim Nicholas Taleb
Published 27 Nov 2012

So the same hidden antifragilities apply to attacks on our ideas and persons: we fear them and dislike negative publicity, but smear campaigns, if you can survive them, help enormously, conditional on the person appearing to be extremely motivated and adequately angry—just as when you hear a woman badmouthing another in front of a man (or vice versa). There is a visible selection bias: why did he attack you instead of someone else, one of the millions of persons deserving but not worthy of attack? It is his energy in attacking or badmouthing that will, antifragile style, put you on the map. My great-grandfather Nicolas Ghosn was a wily politician who managed to stay permanently in power and hold government positions in spite of his numerous enemies (most notably his archenemy, my great-great-grandfather on the Taleb side of the family).

…

For a minute I wondered if I was living on another planet or if the gentleman’s PhD and research career had led to this blindness and his strange loss of common sense—or if people without practical sense usually manage to get the energy and interest to acquire a PhD in the fictional world of equation economics. Is there a selection bias? I smelled a rat and got extremely excited but realized that for someone to be able to help me, he had to be both a practitioner and a researcher, with practice coming before research. I knew of only one other person, a trader turned researcher, Espen Haug, who had to have observed the same mechanism.

…

The edge, I realized, isn’t in the package of what was on the official program of the baccalaureate, which everyone knew with small variations multiplying into large discrepancies in grades, but exactly what lay outside it. Some can be more intelligent than others in a structured environment—in fact school has a selection bias as it favors those quicker in such an environment, and like anything competitive, at the expense of performance outside it. Although I was not yet familiar with gyms, my idea of knowledge was as follows. People who build their strength using these modern expensive gym machines can lift extremely large weights, show great numbers and develop impressive-looking muscles, but fail to lift a stone; they get completely hammered in a street fight by someone trained in more disorderly settings.

Heart: A History
by Sandeep Jauhar
Published 17 Sep 2018

They also had roughly double the rate of cardiac events, including heart attacks, coronary angioplasty, coronary artery bypass surgery, and cardiac-related deaths. Ornish’s study was roundly criticized. It tested a small cohort, reviewers said, hardly representative of the general population. Only half the patients who were invited actually participated, suggesting possible selection bias. Also, virtually none of the patients were on statins or other cholesterol-lowering drugs, so the effect of intensive lifestyle modification on modern, well-treated heart patients was anyone’s guess. Moreover, a study published in 2013 in The New England Journal of Medicine showed that patients who consume a Mediterranean diet rich in olive oil, fruits and vegetables, fish, and nuts had a roughly 30 percent lower risk of cardiac events, including heart attacks and death, than patients advised to follow a low-fat diet, albeit one less extreme than Ornish’s.

…

Petersburg (Russia), 118 Salem (Massachusetts), 117 Salt Lake City, 22, 192 San Francisco Bay Area, 123 Sauerbruch, Ferdinand, 108 schizophrenia, 77n Schmidt, Pamela, 84–85 Schneider, Richard, 104, 107, 108 Scientific American, 127 Scotland, 137, 157, 160, 220 Sears, Roebuck, 78 sedatives, 219 sedentary lifestyle, 123, 241 selection bias, 232 self-experimentation, 102–11, 161 Separation (Munch), 16 septal defects: atrial (ASDs), 77, 78, 94; ventricular (VSDs), 78–80, 83–84, 168 September 11, 2001, terrorist attack, see 9/11 Seroquel, 223 Servetus, Michael, 43 Seuse, Heinrich, 22 Shakespeare, William, 21 Shaw, Laura, 139 shock, 56, 59, 60, 62, 109, 129 shocks, electrical, see defibrillation shortness of breath, 24, 25, 115, 145, 150, 184, 192 Shumway, Norman, 186–88 Siddiqui, Mohammed, 209–10 Siemens echocardiogram machine, 58 Sigmamotor milk pump, 82 Sikhs, 29 Sinai Hospital (Baltimore), 173, 174 Sinemet, 221, 223 sinoatrial node, 151–52, 152, 154 sleep disorders, 210, 219–21, 223–24 smoking, 13, 70, 126, 134, 144, 233, 242; as cardiovascular risk factor, 54, 120–22, 128, 132, 237; rates of, 13, 118, 238 snakes, 7–9, 28 Snow, John,115–17 Snow White (fairy tale), 12 sonar, see ultrasound Sones, Mason, 134–37, 142, 144, 239 Sontag, Susan, 7, 12 South Africa, 183, 186–88 South Asians, prevalence of heart disease among, 122–23, 234 spiral waves, 155, 155–57, 156, 159–60, 217, 242 Stanford University, 187 startle response, 122, 132, 214, 237, 240 statins, 110, 232, 234 stents, 53, 110, 130, 143–44, 220–21, 237 Sterling, Peter, 124–25, 231, 240 Stevenson, Lynne Warner, 189 stimulant drugs, 118 stomach ulcers, 214 strokes, 65, 96, 157, 189, 195, 223; risk factors for, 4, 115, 120, 133, 233 sudden cardiac death, 28, 75, 120–21, 166–67, 176, 226; emotional stress causing, 8–9, 26–28, 31, 129–30, 160, 206–207; prevention of, see defibrillators; ventricular fibrillation as major cause of, 157–58, 171, 192, 211, 220–21, 223–25 superior vena cava, 104, 175, 176 supplements, dietary, 147–48, 165, 181 surgical hypothermia, 77–79, 82 Sweden, 61, 90–91 Switzerland, 11n, 140, 171 sympathetic nervous system, 207, 217, 220–21, 238 syphilis, 105 Syracuse University, 159 Syria, 41 tachycardia, 174 Takens, Floris, 160n takotsubo cardiomyopathy, 24, 24–26, 31, 124 tamponade, 56–57, 61–63, 141 “Termination of Malignant Ventricular Arrhythmias with an Implanted Automatic Defibrillator in Human Beings” (Mirowski), 177 Texas Instruments, 170 Thailand, 220 Threefold Life of Man, The (Böhme), 33 thrifty genes, 234 thrombosis, 37, 97, 114, 129, 232, 239 Time magazine, 84, 95 tissue death, 37–38, 40 Tobacco Institute, 121 Toronto, University of, 93 transposition of the great arteries, 81 treadmill stress test, 4 Trendelenburg, Friedrich, 90 Trost, Dr., 4–5, 233–34 Truman, Harry, 115 tuberculosis, 37, 62, 117 Tufts University, 117 Tulane University, 118 turbulence, 42n, 159–60, 234 type A personality, 126–27 ulcers, 214 ultrasound, 25, 51, 55–56, 58, 60–61, 141, 233; see also echocardiograms Ulysses (Joyce), 140 Unger, Ernst, 107–108 United Kingdom, incidence of heart disease in, 12–13 United States: age at first heart attack in, 122; artificial heart programs in, 189–97; cardiac research in, 87–96, 166–70, 172–77; catheters manufactured in, 141; congenital heart defects in, 75; cost of medical devices in, 149; defibrillators implanted in, 207–16; epidemiological studies in, 113, 116–29; first residency program in, 70; heart transplants in, 187–89; incidence of heart disease in, 12, 123; mortality from heart disease in, 87, 113–15, 157–58, 238; stent use in, 144; see also specific states and municipalities University Hospital (Zurich), 142 “Use of Ultrasonic Reflectoscope for the Continuous Recording of the Movements of Heart Walls” (Edler and Hertz), 61 Utah, University of, Medical Center, 191–92 vaccinations, opposition to, 165 Valium, 219 Vanderbilt University, 189 Variety Club Heart Hospital (Minneapolis), 80 vascular inflammation, 128 vasopressin antagonists, 239 vegetarian diet, 122, 231, 236 vena cava, 63, 104, 175, 176 ventilators, 54, 97, 196 ventricles, 18, 37–38, 40–47, 59–61, 69, 133, 145; of artificial heart, 190, 192–93; congenital defects of, 75, 78–84, 168; electrophysiology of, 151, 152, 166–67; implantable devices attached to, 170–71, 176, 196, 211; premature contractions of, 206, 233, 241; survival of injuries to, 63–65, 68; see also ventricular fibrillation ventricular assist devices (LVADs), 196 ventricular fibrillation, 9, 137, 146, 157–60, 171–75, 175, 179; animal studies of, 175, 175–76, 205–206; as major cause of sudden cardiac death, 157–58, 171, 192, 211, 220–21, 225; see also defibrillators ventricular septal defects (VSDs), 78–80, 83–84, 168 verruga peruana, 105 Vesalius, Andreas, 42, 157n Veterans Affairs Hospital (Buffalo, New York), 169–70 Vibrio cholerae, 116n Victoria, Queen of England, 115 Vietnam War, 121 vigilance, 124, 125n, 137, 240 voltage, 17, 19, 151, 168 voodoo death, 27–28, 31, 160 “‘Voodoo’ Death” (Cannon), 27 “vulnerable period,” 158–59, 161, 167, 173, 175, 179, 206 Wake Forest University, 214 Wang, Thomas, 114 Wangensteen, Owen, 74, 76, 78, 187 Warm Springs (Georgia), 115 Washington, D.C., 65 Washington University School of Medicine in St.

Destined for War: America, China, and Thucydides's Trap
by Graham Allison
Published 29 May 2017

The Thucydidean dynamic is present during the rise, at the point of parity, and after one power has overtaken another. 3. Selection bias: Thucydides’s Trap is guilty of cherry-picking cases to fit its conclusion. It only selected cases that led to war. The Case File includes all the instances we have been able to find in the past years in which a major rising power threatened to displace a ruling power. Because this includes the entire universe of the cases (as opposed to a representative sample), the Case File is immune to charges of selection bias. For a detailed discussion of the Thucydides’s Trap methodology, see http://belfercenter.org/thucydides-trap/thucydides-trap-methodology. 4.

…

In this project we have attempted to include all instances since the year 1500 in which a major ruling power is challenged by a rising power. In technical terms, we sought not a representative sample but the entire universe of cases. Therefore, as The Oxford Handbook of Political Methodology notes: “Insofar as comparative-historical researchers select what can be considered the entire universe of cases, standard issues of selection bias do not arise.” A more detailed explanation of the methodology is available at http://belfercenter.org/thucydides-trap/thucydides-trap-methodology. [back] 2. US Department of State, Papers Relating to the Foreign Relations of the United States and Japan, 1931–1941, vol. 2 (Washington, DC: US Government Printing Office, 1943), 780.

The Panic Virus: The True Story Behind the Vaccine-Autism Controversy
by Seth Mnookin
Published 3 Jan 2012

There were serious problems with its entire premise: Hundreds of millions of children had received the measles vaccine since it was first introduced and the vast majority of them had no chronic bowel or behavioral problems; the “syndrome” Wakefield purported to have discovered was already well documented and was nonspecific to patients with autism; autism was a well-known condition long before the MMR vaccine became available; and despite hypothesizing that the MMR vaccine led to IBD led to autism, in most of the cases Wakefield cited, the behavioral changes preceded the bowel problems. There were methodological problems, the most glaring of which was selection bias: The parents who came to Wakefield, who was not a pediatrician and had never been clinically trained to work with children, did so because he was known as someone interested in connecting the MMR vaccine with inflammatory bowel disease. There were concerns about the reliability of the paper’s data: Wakefield was dependent on parents’ post-facto recollections about the temporal connection between vaccination and onset of their children’s symptoms, and in the three years since Wakefield first reported finding the measles virus in patients with IBD, “other investigators using more sensitive and specific assays, have not been able to reproduce these findings.”

…

Since there was no definitive data about ethylmercury’s half-life (the time it takes for half the amount of a given substance to be eliminated from the bloodstream), Verstraeten used previously established half-life figures for methylmercury in order to estimate the aggregate amount of thimerosal in infants’ bodies at any given point.36 Because of the privacy concerns inherent with using HMO patient records, he’d been unable to question (or even identify) the people whose data he was using—which meant he had no way of accounting for possible selection bias on the part of parents (it was possible that “the same parents that bring their children for vaccination would be the same parents that bring their children for assessment of potential developmental disorders”), or doctors (there was “a potential that certain health care providers use more hepatitis B [vaccine] at birth and would also be more likely to diagnose some of the outcomes”), or both.

…

Some of the others have been alluded to earlier in this book: When SafeMinds members set out to write an academic paper about a hypothesis they already believed to be true, they set themselves up for expectation bias, where a researcher’s initial conjecture leads to the manipulation of data or the misinterpretation of results, and selection bias, where the meaning of data is distorted by the way in which it was collected. In addition to being a natural reaction to the experience of cognitive dissonance, the hardening conviction on the part of vaccine denialists in the face of studies that undercut their theories is an example of the anchoring effect, which occurs when we give too much weight to the past when making decisions about the future, and of irrational escalation, which is when we base how much energy we’ll devote to something on our previous investment and discount new evidence indicating we were likely wrong.

The Book of Why: The New Science of Cause and Effect
by Judea Pearl and Dana Mackenzie
Published 1 Mar 2018

In addition, case-control studies admit several possible sources of bias. One of them is called recall bias: although Doll and Hill ensured that the interviewers didn’t know the diagnoses, the patients certainly knew whether they had cancer or not. This could have affected their recollections. Another problem is selection bias. Hospitalized cancer patients were in no way a representative sample of the population, or even of the smoking population. In short, Doll and Hill’s results were extremely suggestive but could not be taken as proof that smoking causes cancer. The two researchers were careful at first to call the correlation an “association.”

…

It is precisely because we live in the era of Big Data that we have access to information on many studies and on many of the auxiliary variables (like Z and W) that will allow us to transport results from one population to another. I will mention in passing that Bareinboim has also proved analogous results for another problem that has long bedeviled statisticians: selection bias. This kind of bias occurs when the sample group being studied differs from the target population in some relevant way. This sounds a lot like the transportability problem—and it is, except for one very important modification: instead of drawing an arrow from the indicator variable S to the affected variable, we draw the arrow toward S.

…

The experts, who are novices to graphical models, find it easier to configure additional threats than to attempt to remedy any one of them. Language like “miracles,” so I hope, should jolt my colleagues into looking at such problems as intellectual challenges rather than reasons for despair. I wish that I could present the reader with successful case studies of a complex transportability task and recovery from selection bias, but the techniques are still too new to have penetrated into general usage. I am very confident, though, that researchers will discover the power of Bareinboim’s algorithms before long, and then external validity, like confounding before it, will cease to have its mystical and terrifying power.

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
by Valliappa Lakshmanan , Sara Robinson and Michael Munn
Published 31 Oct 2020

Trade-Offs and Alternatives While explanations provide important insight into how a model is making decisions, they are only as good as the model’s training data, the quality of your model, and the chosen baseline. In this section, we’ll discuss some limitations of explainability, along with some alternatives to feature attributions. Data selection bias It’s often said that machine learning is “garbage in, garbage out.” In other words, a model is only as good as the data used to train it. If we train an image model to identify 10 different cat breeds, those 10 cat breeds are all it knows. If we show the model an image of a dog, all it can do is try to classify the dog into 1 of the 10 cat categories it’s been trained on.

…

If we show the model an image of a dog, all it can do is try to classify the dog into 1 of the 10 cat categories it’s been trained on. It might even do so with high confidence. That is to say, models are a direct representation of their training data. If we don’t catch data imbalances before training a model, explainability methods like feature attributions can help bring data selection bias to light. As an example, say we’re building a model to predict the type of boat present in an image. Let’s say it correctly labels an image from our test set as “kayak,” but using feature attributions, we find that the model is relying on the boat’s paddle to predict “kayak” rather than the shape of the boat.

…

all-reduce algorithm, Synchronous training anomaly detection, Anomaly detection-Choosing a model architecture, Handling many predictions in near real time, Fraud and Anomaly Detection Apache Airflow, Solution, Apache Airflow and Kubeflow Pipelines Apache Beam, Batch and stream pipelines, Efficient transformations with tf.transform, Solution-Reduce computational overhead, Solution, Alternative implementations Apache Flink, Solution, Alternative implementations Apache Spark, Batch and stream pipelines, Solution, Alternative implementations Apigee, Model versioning with a managed service application-specific integrated circuit (see ASIC) ARIMA, Problem Representation Design Patterns, Solution arrays, Array of numbers-Array of numbers ASIC, ASICs for better performance at lower cost, Phase 1: Building the offline model asynchronous serving, Asynchronous serving asynchronous training, Asynchronous training-Asynchronous training attribution values, Importance of explainability autoencoders, Autoencoders-Context language models AutoML Tables, Explanations from deployed models AutoML technique, Other ensemble methods autoregressive integrated moving average (see ARIMA) AWS Lambda, Create web endpoint, Lambda architecture, Triggers for retraining, Model versioning with a managed service Azure, ASICs for better performance at lower cost, Model versioning with a managed service Azure Functions, Create web endpoint, Triggers for retraining Azure Machine Learning, Model versioning with a managed service Azure ML Pipelines, Running the pipeline on Cloud AI Platform B bag of words approach (see BOW encoding) bagging, Bagging-Bagging, Why It Works-Bagging, Choosing the right tool for the problem baseline, Model baseline-Explanations from deployed models(see also informative baseline, uninformative baseline) batch prediction, The Machine Learning Process, Phase 2: Building the cloud model, Trade-Offs and Alternatives batch serving, Cached results of batch serving-Lambda architecture Batch Serving design pattern, Design Patterns for Resilient Serving, Design Pattern 17: Batch Serving-Lambda architecture, Pattern Interactions batch size, Choosing a batch size-Choosing a batch size batching, Fully managed Bayesian optimization, Bayesian optimization-Bayesian optimization beam search algorithm, Problem Representation Design Patterns BERT, Context language models-Context language models, Embeddings of words versus sentences, Choosing a batch size biasdata, Data selection bias, Problem, Before training-Allow and disallow lists(see also data collection bias, data distribution bias, data representation bias, experimenter bias, implicit bias, problematic bias, proxy bias, reporting bias) human, Data Quality-Data Quality, Problem(see also implicit bias, problematic bias, proxy bias, ) model, Images as tiled structures, Solution, Boosting, Weighted classes, Problem, Limitations of explanations, Problem-Before training, Data augmentation-Fairness versus explainability(see also label bias) unfair, Allow and disallow lists bias-variance tradeoff, Problem, Choosing the right tool for the problem Bidirectional Encoding Representations from Transformers (see BERT) BigQueryabout, What Are Design Patterns?

Investing Amid Low Expected Returns: Making the Most When Markets Offer the Least
by Antti Ilmanen
Published 24 Feb 2022

More recently, CEM Benchmarking surveyed large institutional investors, and found total fees to be 5.7% p.a. comprising 2.7% in management fees, 1.9% in carried interest (performance fee), and 1.2% for other fees, including net portfolio company fees (see Doskeland-Stromberg (2018)). Coinvestments and direct investments can have clearly lower fees, but the deals may suffer from selection bias (the “lemons” problem), and Ivashina-Lerner (2019) find no higher net returns to end-investors from such arrangements than from the more costly PE funds. 10 See Ilmanen-Chandra-McQuinn (2020). This positive verdict has gone through various gyrations over time in the academic literature. Kaplan-Schoar (2005) and others showed that buyout firms did not outperform the S&P500 after fees in the long run, which I summarized as a PE-cautious consensus view in Ilmanen (2011).

…

Positive autocorrelation (momentum bias) also tends to make underperformance more likely, while mean reversion has the opposite impact (but these impacts are small for most investments). 6 Another approach asks how many years of evidence is needed for statistical significance. Under standard assumptions (no non-normality or selection bias), statistical significance at the 95% confidence level requires a t-statistic near 2.0. Since the t-statistic is a product of realized Sharpe ratio and time period (the square root of years), a performance history with a Sharpe ratio of 0.4 will be significantly different from zero (with 95% confidence) after about 25 years (0.4 * √25 = 2), while a Sharpe ratio of 0.7 will be significantly different from zero after roughly 8 years (0.7 * √8 ≈ 2). 7 There are other entirely good reasons for changing allocations after losses.

…

Babu, Abhilash; Brendan Hoffman; Ari Levine; Yao Hua Ooi; Sarah Schroeder; and Erik Stamelos (2020), “You Can't Always Trend When You Want,” Journal of Portfolio Management 46(4), 52–68. Bacchetta, Philippe; Elmar Mertens; and Eric van Wincoop (2009), “Predictability in financial markets: What do survey expectations tell us?” Journal of International Money and Finance 28(3), 406–426. Bailey, David; and Marcos Lopez de Prado (2014), “The deflated Sharpe ratio: correcting for selection bias, backtest overfitting, and non-normality,” Journal of Portfolio Management 40(5), 94–107. Bain (2021), Global Private Equity Report 2021. Bakshi, Gurdip S.; and Zhiwu Chen (1994), “Baby boom, population aging, and capital markets,” Journal of Business 67(2), 165–202. Bali, Turan G.; Nusret Cakici; and Robert F.

The Hidden Half: How the World Conceals Its Secrets
by Michael Blastland
Published 3 Apr 2019

Don’t think levers connected to an obedient world; think of a wind of the crank on a Mouse Trap game. 11. Treasure your exceptions. Stories or anecdotes can be treacherous, and this book is full of them. Statistician Andrew Gelman of Columbia University in the US, writing with Thomas Basbøll from Copenhagen Business School, says they can be ‘a prime case of selection bias’; an excuse ‘for choosing the amusing, unexpected, and atypical rather than the run-of-the-mill boring reality that should form the basis for most of our social science’.12 Another statistician, David Spiegelhalter, says we need an immunity to misleading anecdote. One story can never stand as a general rule.

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Index abstract formulas 141 Academy of Medical Sciences 133 adoption studies 41 aid, economic development 141 aid-effectiveness craze, the 153 alcohol consumption 180 AllTrials campaign 114–5 Altman, Doug 129–30 Amano, Yukiya 185 ambiguity 209–10 Amgen 111–2 Analysis (radio programme) 102 analytic validity 158, 263n18 anarchy 224 aphorisms 68–9, 149 apprenticeships 205–6 argument, beliefs and habits of 186 asthma 135 Attanasio, Orazio 225–9, 230 Autho, David 219–23 average knowledge 173 background influences 23–34 background norms, rejecting 24–5 bacon 161–3, 162–3 Banerjee, Abhijit 150–4, 157 Bangladesh 80–2, 82, 101–2, 158, 261n6 Bank of England 103, 216 Bank of Japan 103 Basbøll, Thomas 244–5 baseline data 165 base-rate neglect 176–7 basic laws 140 Bateson, William 245 BBC 88, 98 Beatles, the 52–3, 259n33 Begley, Glenn 111–7 behaviour context-specific 42–3 environmental cues 65–7 behavioural economics 157 Behavioural Insight Team 155, 156, 232 beliefs 60 contradictory 63–4 inconsistency of 60–6 justification 60–1, 63 manipulation 62–3 power of information on 66–8 self-contradiction 61–2 Berlin, Isaiah 199 betting, on knowledge 236–7 big causes, power of 35 big events causal intricacy 193–6 complexity 185–7 difficulty determining causality 188–96 power of circumstance 196–9 big picture, the 215–6 Bijani, Ladan 40–1 Bijani, Laleh 40–1 biographies 49 biological randomness 43–4 biomedical science, research standards 129–36 Bolsover 217–8 Boorstin, Daniel 17, 136, 138, 264n24 Booth, Charles 146–7 BP 211 brain, the 64 plasticity 56 self-justifying 83 breast cancer 45–6, 46 Brexit referendum 18–9, 20, 90, 214–8, 223–4, 241 Bunnings 77 Burckhardt, Jacob 255n20 Burke, Edmund 269n1 Burns, Terry 102–3 business decisions, failures 210–1 cancer 45–8 breast 45–6, 46 lung 174–5 risk 162–3, 166, 174–5 screening 132–3 Cancer Research UK 133 canned laughter 154–5 capitalism 118 Carillion 211 Carp, Joshua 123–4 Cartwright, Nancy 79, 79–82, 82, 193–4, 195, 202–3, 203–4, 263n18 causal instincts 123 causal interactions, complexity 239 causal intricacy 193–4 causal models 242–4, 243, 269–70n3 causal theorizing 212–4 causality assumption of 212–4 difficulty determining 188–96 existence of 276–7n12 hard 225–9 importance of 212 mechanical models 242–4, 243 in one person 48 cause and effect dependable 203–4 patterns of 23, 25–6, 26 supposed 248 unreliable 204 causes and causal influences 90, 94 competing 248 criminals 29 interaction 193–6 and luck 178 secret life of 8–11 simple 184–5 cells, biographical stories 47–8 certainty, desire for 235 Chadwick, Edwin 146–7 chance 14, 37–8, 247, 281n1 chaos theory 56–7, 276n10 Chater, Nick 59, 60, 63, 64–5, 66–7 Chernobyl disaster 185 child and adolescent development 23–6, 41–2 child mental health 206–7 childhood influences 23–5 delinquent boys 26–34 China, rise of 218–23, 279n19 choice, situated 31–3, 34 choice blindness 62 choices 60 Cialdini, Robert 154–5 Cifu, Adam 131–2 circumstances 70 power of 196–9 claims inflation 130 climate change 238–9 Clinton, Hillary 222 Cochrane Collaboration, the 189–90 cognition 64 cognitive biases 14 cognitive limitations 14, 214 Comaroff, John 107–8 common sense 69–70 comparative cost analysis 173 competence 236–7 complacency 237 complexity adding 244 big events 185–7 facing 15 hidden 184–201 of reality 245 complexity theory 276n10 complexity-avoidance 187 complications, hidden 187 Conan Doyle, Arthur 108 confidence 72 consistency 68–75, 202–4, 260n6, 260n8 constructive realism 17 consumer behaviour 77 context 41–2, 72, 101 context-specific behaviour 72 context-specific learning 42–3 control alternative to 248–9 elusiveness of 85–6 powers of 195 conviction 104 coping strategies 16–7, 225–46 adapting 230–3 betting 236–7 communicate uncertainty 237–9 embracing uncertainty 234–6 exceptions 244–5 experiment 230–3 governing for uncertainty 239–41 managing for uncertainty 241–2 metaphors 242–4 negative capability 234 relax 246 triangulation 233–4 use of probability 242 Corbyn, Jeremy 20 corporate power 241 cost/benefit analysis, cows 117–22 cows, cost/benefit analysis 117–22 Coyle, Diane 216, 262n12 Crabbe, John 85–7 credibility 238–9 credibility crisis 18 crime causes of 142–4 heroes and villains view 142 opportunist 144–5 reduced opportunity 144–5 theory of 142–6, 143 victims and survivors view 142–3 criminals causal influences 29 childhood influences 26–34 desisters 30 high rate chronics 30 life-course persistent offenders 28–9 life-courses 28, 236 variables 31 critical factors 83–5 crowds, wisdom of 149 cultural difference 79–82, 79–85 Daniels, Denise 43–4, 57 Darwin, Charles 50–1 data granularity 216–7 interpretation 98–100 Dawid, Philip 276–7n12 De Rond, Mark 198, 201 de Vries, Ymkje Anna 114 deadweight cost 205–6 debate 98 decision making 58–60 influences 32–3 situated choice 31–3 deep preferences 65 deeper rationale, construction of 60 Deepwater Horizon 211 defining characteristics 43 degrees of freedom 122–9 delinquent boys 26–34 dementia 176–7, 274n16 democracy 20 Deng Xiaoping 219 Denrell, Jerker 199, 201 desires 59 details importance of 49–54 neglecting 151–2 problem of 229 selective 26 determinism 28 development economics 150–3 developmental difference, sources of variation 9–11 developmental noise 10 difference 15 pockets of 214–24 Dilnot, Andrew 237, 275n3 disciplined pluralism 231 disorder 45 forces of 11–3 doubt 238 Down’s syndrome 166 drugs comparative cost analysis 173 impact 171–2 medical effect 167–9, 169, 170–4 non-responders 172 Numbers-Needed-to-Treat (NNTs) 168, 169, 170, 173–4 predictive weakness 170–3 duelling certainties 235 Duflo, Esther 83, 84, 141, 150–3, 157–8, 158–9, 230–1 ecological validity 263n18 economic development, aid 141 economic forecasting 92, 102–7 economic recovery 217–8 economics 233 economy, the 87–100, 91, 93, 94, 95 education 151–2, 206–7, 275– 6n7 Einstein, Albert 140–1 Emerson, Ralph Waldo 68 enigmatic variation 13–6, 48 environment context 72 non-shared 37 shared 35 environmental influences 43–4 epidemiology 181 epigenetics 6–7 erratic influences 60 essential you, the 59–60 estimates 89–91, 96 European Central Bank 103 evidence 21 balance of 114 conclusive 186, 187 the Janus effect 121, 122–9 limitations of 117–22 statistical significance 137 strength of 137 evidence-based medicine 133–4 exceptions 214–24, 244–5 expectations 35 big 196 frustration of 15 of regularity 47, 202–4 unrealistic 182 experience, influence of 33, 34, 55–7 experiment 230–3 expertise, crisis of 18–9 experts, credibility crisis 18–9 external validity 101, 158, 263n18, 264n19 extreme performance 199 failure 204–11 fairness 66–7 false negatives 113–4 false positives 113–4, 122 falsification 245 family, changes of 41 farmer and a chicken, the 202–4 fate 30 fears, exaggerated 46 Financial Times 77 First World War 108 Fitzroy, Robert 50 flat mind, the 60, 60–8 Flaubert, Gustave 139 forecasting 109 former Yugoslavia 108 foxes 199 France 186–7 Freedman, Sir Lawrence 108, 109 freedom 236 Fukushima nuclear power station meltdown 185–7 fundamentals 141 identifying 153 further education 208–9 Galbraith, John Kenneth 110 Gartner, Klaus 87 Gash, Tom 142–3 Gates, Bill 199 GDP data 262n12 growth estimation 88–100, 91, 93, 94, 95, 262–3n14 local 214–5, 216, 218 Gelman, Andrew 124–5, 244 gene–environment interaction 6–7 general principles 140 generalities 174 generalization 76–8, 146, 152, 263n18 genes and genetics influence of 34–7, 39–41, 44, 45–7 overclaiming 134–5 power of 33, 45 genetic risk 45–7 genius, dangerous 212–4 genotype 8 Germany 185, 186, 188 Gillam, John 77 global financial crisis, 2008–9 104, 106, 210, 235 globalization 213 Gove, Michael 18–9 granularity 216–7 ground truth 217 groupthink 149 guarantees, lack of 160 Guardian 207 Gupta, Rajeev 117, 118 Haldane, Andy 216–7, 218 Harford, Tim 156–7, 237 Harris, Judith Rich 40–2, 72 Hayek, Friedrich 105–6 health screening 177 heart disease 163–6 hedgehogs 199 Henry (ex-delinquent) 32 Hensall, Abigail 39–40, 41 Hensall, Brittany 39–40, 41 herd mentality 154–5 hidden causes 35–8 hidden half, the coping strategies 225–46 ignoring 202–24 mystery of 35 power of 44–5 hidden trivia 8–9 hindsight 78 hindsight bias 83 history 107–8 lessons of 109 Homebase 76–7 Honda, US motorcycle market penetration 196–9 hubris 77 human sameness irregularity 45–9 limits of 34–45 human understanding, fundamentals 213 Human Zoo, The (radio programme) 60–6 humility 224, 248–9 IBM 199 ibuprofen 163–5 ideological divide 240 ideologies 9–10 idiosyncratic influence 53–4 ignorance 21, 107 disguising 242 the shock of 7 imagination 138 impulsive judgement, value of 149 incarceration rates, United States of America 222, 240, 280n10 incidentals, effect of 51–2 incoherency problem, the 149 inconsistency beliefs 60–6 justifiable 70–1 incredible certitude 209 Indian Express 117 individual differences 56 individuality conjoined twins 39–42 neurological foundation of 56 industrial policy 208 inflation 102–7 influences background 23–34 childhood 26–34 criminals 26–34 decision making 32–3 environmental 43–4 erratic 60 hidden 204 microenvironmental 8–9, 253–4n12 information power of 66–8 selective 66–7 Institute for Fiscal Studies 205–6 Institute for Government 208–9 intangible differences 253n11 intangible variation 10, 229 interaction, problems of 193–6 internal validity 101–2, 158 International Journal of Epidemiology 43 intuition 54, 204 Ioannidis, John 121, 133–6 irrationality, human 14 irregularity 94 disruptive power of 224 frustration of 15 human 45–9 influence 12 problem of 229 underestimating 214–24 Islamic State 108 it’s-all-because problem 91, 96 James, Henry 29, 56 James, William 141 Janus effect, the 121, 122–9 Johansen, Petter 62 Johnson, Samuel 214 Johnson, Wendy 71–2 Jones, Susannah Mushatt 162–3, 165 journalism 237–8 Juno (film) 193 Kaelin, William 130 Kawashima, Kihachiro 197 Kay, John 16, 68, 197, 231, 232 Keats, John 138–9, 234 Kempermann, Gerd 56, 57 Keynes, John Maynard 107, 271n9 Keynesianism 103 King, Mervyn 103, 104, 106, 110 Kinnell, Galway 28 Knausgaard, Karl Ove 86–7 Knight, Frank 107 Knightian uncertainty 107 knowledge 12–3, 170 advance of 20–1 average 173 betting on 236–7 credibility crisis 18 critical factors 83–5 failures of 19, 76–8, 79–82 fallibility of 248 generalizable 234 generalization 76–8 illusion of 136, 138 lessons of the past 102–7, 107–10 in medicine 182 negative capability 138–9 as obstacle to progress 17 obvious 82 paths to 136–9 plausibility mistaken for 132 practical 30–1 pretence of 105–6 probabilistic 160, 161, 163–4, 172–3 and probability 180 problem of scale 177–80 provenance 116 relevant 82–5 replication crisis 111–7 subverting 76–110 and time variations 87–100, 91, 93, 94, 95 transfer 37, 76–8, 83, 101–2 unknowns 85–7 validity 100–2 validity across time 107–10 weakest-link principle 79–82 Krugman, Paul 210 Lancet 225–6 Langley, Winnie 51, 165, 178 Laub, John 26–34, 42 law-like effects, claims about 21 learning styles 207 Leicester City Football Club 199–201 Leon (ex-delinquent) 31–2 Leyser, Ottoline 114 life, mechanics of 51 life-course persistent offenders 28–9 limits and limitations 16–7, 44, 75 base-rate neglect 176–7 of cleverness 278n14 individual level 174–6, 178–9, 181–3 lack of guarantees 160 marginal probabilistic outcomes 176–7 medical effect 167–9, 169, 170–4 on prediction 165–6 on probability 160–83 problem of scale 161–6, 174– 6, 177–80, 181–3 Liskov Substitution Principle 261n3 Little Britain (TV comedy) 192 Liu, Chengwei 198, 201 lives, understanding 29 location shift 264n20 Loken, Erik 124–5 long-acting reversible contraceptives (LARCS) 190 luck 37–8, 48, 178, 198 lung cancer 174–5 Lyko, Frank 1, 2 machine mode thinking 151–2 Macron, Emmanuel 20 Manski, Charles 209, 235 Mao Zedong 218 marginal probabilistic outcomes 176–7 marmorkrebs 1–9, 4, 10, 12, 12–3, 22, 35, 81, 182, 252n2 Marteau, Theresa 65 Martin, George 52 May, Theresa 208 Mayne, Stephen 77 measurement 99–100 mechanical relationships 212, 242, 244 mechanical thinking 242–4, 243 media stigma 192–3 medical effect, drugs 167–9, 169, 170–4 medical reversal 131–3 medicine comparative cost analysis 173 knowledge in 182 non-responders 172 Numbers-Needed-to-Treat (NNTs) 168, 169, 170, 173–4 personalized 181–3 predictive weakness 170–3 probability and 167–9, 169, 170–4 memory 56, 102–7 Mendelian randomization 233 Menon, Anand 214–5 mental shortcuts 14–5 mere facts 202–3 meta-science 19, 20 methodological revisions 97–8, 120 mice 55 microenvironmental influences 8–9, 253–4n12 micro-irregularity 35–7 micro-particulars 128 Microsoft 147–50, 199 Miller, Helen 66–7, 67 mind, the flat 59–60, 60–8 shape 59 models and modelling 140, 242–4, 243, 269–70n3 moment when, the 52 morality, changing 108 More or Less (radio programme) 237 Munafò, Marcus 234 Nadella, Satya 147–8 National Survey of Family Growth 192 National Surveys of Sexual Attitudes and Lifestyles 191–2 nationalism 108 Nature 2, 112, 136, 168, 174 nature/nurture debate 3, 5–6, 9–10 negative capability 138–9, 234 neurology 58 New England Journal of Medicine 131–2 Newcastle upon Tyne 214 Newton, Isaac 140–1 noise 14 definition 10 developmental 10 as intellectual dross 11 re-appraisal of 11–3 non-shared environment 37 Nosek, Brian 129 noses 49–51 Nottingham 217 Numbers-Needed-to-Treat (NNTs) 168, 169 nurture, influence of 44 O’Connor, Sarah 217–8 Office for National Statistics 89, 92, 98, 99–100, 216 O’Neill, Onora 238 opinions 21, 59 order 11–2, 13 organ donation campaign 155–6 outside influence 44 overclaiming 134–5 overconfidence 21 overseas business expansion 76–8 Oxfam, sexual abuse scandal 210 Paphides, Pete 52–3 parental behaviour 41 parents, impact of 41 Parris, Matthew 63 parthenogenesis 1–2 particularism 271–2n15 particularity problem, the 93 past, the, lessons of 102–7, 107–10 pattern-making instinct 21 patterns 13 pendulums 57 perceptual systems 64 performance 72–5 personalized medicine 181–3 Peto, Richard 47–8 phenotypes 8 physiognomy, and character 50 plausibility 132 Plomin, Robert 43–4, 49, 57 pluralism 231–2 polarization 235 policy making 231–2 appraisal 277n4 chances of success 208 failures 204–9 governing for uncertainty 239–41 and probability 178–9 secret of 209 seminar 207–8 sequential changes 208 political assumptions, fall of 20 political beliefs 60–6 population validity 263n18 populism, rise of 20 poverty 240–1 Prasad, Vinayak 131–2 precision 183 predictability 28 predictive weakness 165–6, 170–3 preferences 59, 62 deep 65 priming 126–8 probabilistic knowledge 160, 161, 163–4, 170, 172–3 probability 54, 70, 107, 258n25, 272n2 advantages 177–80 base-rate neglect 176–7 difference in 30 fear of low probabilities 166 individual level 174–6, 178–9, 181–3 limits and limitations 160–83 marginal 176–7 medical effect 167–9, 169, 170–4 paradox 170 and policy making 178–9 predictive weakness 165–6 problem of scale 161–6, 174– 6, 177–80, 181–3 recognizing significance 161 risk evaluation 161–6 suggestion of knowledge 180 use of 242 usefulness 161 problems, conceptualizing 17 productivity growth 209–10 progress, knowledge as obstacle to 17 psychoanalysis 58 psychology 58 Pullinger, John 278n14 Pullman, Philip 37 quantification, risk and risk-taking 162–5 racism 125–6 radical uncertainty 106, 107 Radio, Andrew 102 rage to conclude, the 139 randomized controlled trials, value of 280n6 randomness, pure 9 Ranieri, Claudio 200–1 rationality 68, 260n6, 260n8 reality 230, 245, 254n14 reciprocity 155 reflection 65–6 regularity 73, 160 assumption of 212–4 expectations of 47, 202–4 search for 212, 230 statistical 240–1 replication crisis 18, 111–7, 117– 22, 129, 136, 138 Replication Project 129 research 111–39 balance of evidence 114 breadth 130 claims inflation 130 confidence in 115–6 credibility crisis 18 decision rules 136–9 depth 130 evidence-based medicine 133–4 false negatives 113–4 false positives 113–4, 122 fragility 128–9 freedom 122–9 half wrong 113, 115–6 the Janus effect 121, 122–9 limitations of 117–22 micro-particulars 128 multiple analyses 125–6 multiple conclusions 117–22 overclaiming 134–5 priming 126–8 redemption 20 replication crisis 111–7, 117– 22, 129, 136, 138 rigour 19 scepticism 115–6 standards 129–36 statistical significance 122 triangulation 138 validity 101–2 research-credibility crisis 18 rigour 19, 246 risk and risk-taking 70–1, 107, 186 alcohol consumption 180 cancer 162–3, 166, 174–5 communication of 133 evaluation 161–6 heart disease 163–6 quantification 162–5, 166 quantified 187 risk-perception 71 Rockhill, Beverly 181 Rolling Stone magazine 23 Rose, Geoffrey 175–6 Rowntree Joseph 146–7 Royal Bank of Scotland 211 Russell, Bertrand 202, 202–3 samples, validity 100–2 Sampson, Robert 26–34, 42, 236 sanitation 225–9 Santayana, George 109 scale, problem of 161–6, 174–6, 177–80, 181–3 scepticism 105, 115–6, 128, 206 schizophrenia 34–6, 256n10 Science 56 Scientific American 55 Scotland, Triple-P parenting programme 206 screening 132–3, 177 searing memory, doctrine of the 102–7 selection bias 244 self-understanding 67 Sense about 115 serendipitous events 43, 52–3 sex education, role of 189–90 short-term gene–environment interaction 7 significance, recognizing 161 Silberzahn, Raphael 125–6 Simmons, Joseph 122–3 situated choice 31–3, 34, 42 situations, appraisal of 72 sliding-doors moments 50 small differences, power of 56–7 small effects, influence of 49–54 small experiences, influence of 35–7 smartphones 97, 191 Smith, George Davey 50, 51, 234, 281n1 social contexts 31, 195 social media 191 social mobility 240–1 social policy 195 social proof 154–6 social reformers 146–7 social science, utility of 146–50 special theory of relativity 140–1 Spiegelhalter, David 180, 244–5 spontaneous interaction 9 stagflation 103 statins 171 statistical regularities 240–1 statistical significance 122, 137 stents, use of 131 stories and storytelling 25–6, 53–4, 244–5, 247, 248, 258n25, 258n27 structural forces 54 Sun, the 51 support factors 194 Surfers Against Sewage 70–1 surgeons, skills 73–4 system 1 thinking 149 systematic forces 54 systems-level thinking 153 Tamil Nadu 79–82, 101–2 Tangiers, Morocco 84 technology, changing 108 Teen Mom (TV show) 193 teenage pregnancy rate decline in 184, 188–96 estimates 275n3 terrible simplifiers 255n20 Tesco 77, 211 Thaler, Richard 157 theories 140–59 analytic validity 158 arguments about 150–4 of crime 142–6, 143 development economics 150–3 fitness 157 implementation 152 limitations 157 and practice 141 refining 156–7 relevance 157–8 social science 146–50 tension in 154–9 using 156–7 ‘thick’ description 86 time, validity across 107–10 Time magazine 193 time variations, and knowledge 87–100, 91, 93, 94, 95 The Times 63 toilets 225–9 Toshiba 211 trade-offs 190–1 trends 54 trials 156 triangulation 138, 233–4 Triple-P parenting programme 206–7 trivia, importance of 84–5 true uncertainty 107 Trump, Donald 20, 218, 222, 223–4 trust 238 trust deficit 218 trustworthiness 238 Tufte, Edward 139 turning points, variety 49–54 TV crime shows 143, 143 twins and twin studies conjoined 39–42 identical 34–7, 39, 256n10 Tyson, Mike 23, 23–6 Tyson, Rodney 24–5, 255n3 Uhlmann, Eric 125–6 uncertainty 89–90, 100, 209– 12, 254n14 admitting 238 communicating 237–9 data 89–91 embracing 234–6 erratic 93 governing for 239–41 Knightian 107 language of 238 managing for 241–2 in medicine 167–9, 169, 170–4 perpetual 230 radical 106, 107 true 107 uncertainty laundering 268n33 understanding hidden half of 13 limiting effects on 14 limits of 54 unemployment 221–2, 263n17 unintended consequences 105, 229 United States of America China trade 220–3 incarceration rates 222, 240, 280n10 labour market 221 minimum wage 266–7n10 unemployment 221–2 universal gravitational attraction, theory of 140–1 unknowns 85–7, 206 unusual, the 195 upbringing 23–5 Uyeno, Lori 47 validity across time 107–10 analytic 158, 263n18 ecological 263n18 external 101, 158, 263n18, 264n19 internal 101–2, 158 knowledge 100–2, 107–10 population 263n18 research 101–2 samples 100–2 values 59, 232 variation, sources of 5–8 Volkswagen, diesel emissions scandal 211 Wall Street Journal 219 Wallace, Alfred Russel 259n33 Walmart 77 Watts, Duncan 68, 69, 147–50 weakest-link principle 79–82 Wedgwood, Josiah 50–1 Wellington, Duke of 51 Wesfarmers 76–7 West Germany, motorcycle thefts 142–4 Western, Bruce 54 Wilson, Harold 99 World Bank Independent Evaluation Group 79 World Health Organization 162 world picture 63–4 Wright, Sewall 253n11

Data Wrangling With Python: Tips and Tools to Make Your Life Easier
by Jacqueline Kazil
Published 4 Feb 2016

Using an API Pros Cons Immediate access to data you can use Unreliability of mass API system (selection bias) Vast quantities of data Data overload You don’t have to worry about storage; you can just access the data from Reliability and dependence on access—API the service’s storage limitations or downtime As you can see, there are benefits and compromises. If you find an API you want to use, create a few rules around how you will use it and what to do if it is not accessible (you may want to store responses locally to avoid downtime issues). Collecting enough responses over time can also help eliminate some selection bias in your research. Outside of social web services, there are a variety of sites where you can post your own questions and ideas and ask for a crowdsourced reply.

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We also know they have a standard protocol for training their workers on how to prop‐ erly conduct the interviews. These are all good signs that the data is a proper sample and not a pre-selected sample. If, instead, we found out that UNICEF only inter‐ viewed families in large cities and ignored the rural population, this might result in a selection bias or sampling error. Depending on your sources, you should determine what biases your dataset might have. You can’t always get perfect data. However, you should be aware of what sampling biases your data might have and ensure you don’t make sweeping claims based on datasets that might not represent the entire story or population.

Winning the War on War: The Decline of Armed Conflict Worldwide
by Joshua S. Goldstein
Published 15 Sep 2011

The dearth of official statistics on security issues stands in stark contrast to the vast amount of government data (on development, health, education, etc.) that track progress . . . toward meeting the 2015 Millennium Development Goals. . . .” The headlines and news stories from particular wars cannot be trusted to show trends, because of what social scientists call “selection bias.” This means that we study something by looking more at one of the outcomes we are studying than at another. War reporting suffers from such a bias. Whenever a war breaks out, the world’s journalists flock there to tell us of its horrors. From the perspective of that time and place, war is an unmitigated disaster.

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If, for example, the number of people killed in wars declined from 1 million in a certain time period to 100,000, over several generations, “reporters are still able to find plenty of images of violence, for . . . 100,000 people are still dying.” Thus, “the public will never suspect this change” to a dramatically more peaceful world. Indeed, as more reporters with better technology cover the world’s extremes more completely, violence will seem to be increasing. Selection bias is a serious problem. It is hard to make rational policies based on large numbers since we are drawn to the few dramatic cases. It is, on the contrary, easy to presume that war is inescapable. Editors, for their part, play up the horrors of war because dramatic conflicts bring in readers and capture our attention.

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Melander, Öberg, and Hall 2009; see also Sarkees and Wayman 2010: 559; Lacina and Gleditsch 2005: 154. 236 Quickly defies: Lacina and Gleditsch 2005: 148; see also Collier 2009: 4. 237 Have become rare: Wallensteen and Sollenberg 1996: 356; Harbom and Wallensteen 2009: 578, 579. 237 Djibouti: BBC News 2008a. 237 Georgia and its breakaway: Harbom and Wallensteen 2009: 579–80. 237 Diminished as well: Levy 2002: 351. 238 Trend has flattened out: Hewitt, Wilkenfeld, and Gurr 2007; Harbom and Wallensteen 2009. 238 Ripple of interest: Easterbrook 2005; Mack 2005; Tierney 2005; Noah 2005; Sands 2005; Kaplan 2006; Traub 2006b; Arquilla 2006. 239 Steady downward trend: Wilson and Gurr 1999; see also Goldstein 2002. 239 Not seem to get through: Mack 2007: 523, 524; see also Human Security Centre 2005: 18. 239 Selection bias: Licklider 2005: 37. 239 Reporting the worst: Boulding 1978: 83. 240 Plenty of images: Payne 2004: 13; see also Taleb 2007: 112, 55, 80, 100. 240 Bleeds, it leads: See Boulding 1978: 83. 240 Progress Paradox: Easterbrook 2003: 35, 36. 240–41 Much more peaceful: Payne 2004: 7. 241 Chronological bias: He used this nice phrase at a conference, but calls it “presentism” in Payne 2004: 8. 241 Tendency to assume: Payne 2004: 68, 8, 9. 241 Takes to task: Payne 2004: 14, 9, 69, 267 n.3; Richardson 1960: 112, 128; Luard 1986: 23. 242 Glorify violence: Pinker 2007: 20. 242 Lack moral concern: Payne 2004: 10–11. 242 High-decibel: Easterbrook 2003: 100. 242 Followers and donations: Pinker 2007: 20. 242 The generation: Toynbee 1954: 322. 243 Researchers in Vancouver: Human Security Centre 2005: 17, 36, 28, 41, 42, 44. 244 One area where things: Human Security Report Project 2007: 28, 38–39, 35, 36, 42, 43. 244 Number of terrorist attacks: Human Security Report Project 2007: 2–3.

The Case Against Education: Why the Education System Is a Waste of Time and Money
by Bryan Caplan
Published 16 Jan 2018

Black, Smith, and Daniel 2005, p. 429 (columns 4 and 8, quartile estimates), and Black and Smith 2004, p. 114, (column 5, results with years of education controls). 86. See, e.g., Horn 2006. 87. For general discussion and detailed references, see Pascarella and Terenzini 2005, pp. 387–89. Alon and Tienda 2005 and Titus 2004 present typical results. 88. Heil et al. 2014 reports that, after correcting for many preexisting student traits and selection bias, college selectivity at least does not raise completion probability. 89. My estimates also assume college quality has the same proportional effect on unemployment; i.e., if high quality raises compensation by 10%, it also cuts unemployment by 10%. 90. Master’s degree computations continue to assume a master’s year costs the same as a bachelor’s year. 91.

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Fong, Geoffrey, David Krantz, and Richard Nisbett. 1986. “The Effects of Statistical Training on Thinking about Everyday Problems.” Cognitive Psychology 18 (3): 253–92. Frank, Robert. 1999. Luxury Fever: Money and Happiness in An Age of Excess. Princeton, NJ: Princeton University Press. Frazis, Harley. 1993. “Selection Bias and the Degree Effect.” Journal of Human Resources 28 (3): 538–54. ———. 2002. “Human Capital, Signaling, and the Pattern of Returns to Education.” Oxford Economic Papers 54 (2): 298–320. Freund, Philipp, and Heinz Holling. 2011. “How to Get Really Smart: Modeling Retest and Training Effects in Ability Testing Using Computer-Generated Figural Matrix Items.”

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Combining Individual and National Variables to Explain Subjective Well-Being.” Economic Modelling 20 (2): 331–60. Hemelt, Steven, and Dave Marcotte. 2011. “The Impact of Tuition Increases on Enrollment at Public Colleges and Universities.” Educational Evaluation and Policy Analysis 33 (4): 435–57. Hendricks, Lutz, and Oksana Leukhina. 2014. “The Return to College: Selection Bias and Dropout Risk.” CESifo Working Paper Series 4733. Last modified April 5. http://ssrn.com/abstract=2432905. Henrichson, Christian, and Ruth Delaney. 2012. “The Price of Prisons: What Incarceration Costs Taxpayers.” Federal Sentencing Reporter 25 (1): 68–80. Hérault, Nicolas, and Rezida Zakirova. 2015.

Bad Data Handbook
by Q. Ethan McCallum
Published 14 Nov 2012

administrative data, compared to survey data, Subtle Sources of Bias and Error–Subtle Sources of Bias and Error bottomcoding in, Subtle Sources of Bias and Error, Other Sources of Bias, Topcoding/Bottomcoding–Topcoding/Bottomcoding CityGrid API for, Getting Reviews flaws in, Example 1: Defect Reduction in Manufacturing–Example 1: Defect Reduction in Manufacturing imputation bias in, Subtle Sources of Bias and Error, Imputation Bias: General Issues–Imputation Bias: General Issues proxy reporting in, Other Sources of Bias, Proxy Reporting reporting errors in, Reporting Errors: General Issues–Reporting Errors: General Issues sample selection bias in, Other Sources of Bias, Sample Selection seam bias in, Other Sources of Bias, Seam Bias topcoding in, Subtle Sources of Bias and Error, Other Sources of Bias, Topcoding/Bottomcoding–Topcoding/Bottomcoding understanding, importance of, A Very Nerdy Body Swap Comedy–A Very Nerdy Body Swap Comedy web-scraping, Other Formats–Other Formats, (Re)Organizing the Web’s Data–(Re)Organizing the Web’s Data, Can You Get That?

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(see RPM) reviews and ratings data, Getting Reviews, Getting Reviews–Sentiment Classification, Sentiment Classification, Polarized Language–Polarized Language, Corpus Creation–Corpus Creation, Training a Classifier–Lessons Learned collecting, Getting Reviews corpus for classification of, Sentiment Classification, Corpus Creation–Corpus Creation polarized language in, Polarized Language–Polarized Language sentiment classification of, Getting Reviews–Sentiment Classification, Training a Classifier–Lessons Learned robots.txt file, robots.txt–robots.txt RPM (Revenue Per 1,000 Impressions), Is It Just Me, or Does This Data Smell Funny? S S expressions, File Formats sample selection, bias in, Other Sources of Bias, Sample Selection Schwabish, Jonathan A. (author), Subtle Sources of Bias and Error–Conclusions ScraperWiki, Can You Get That? screen-scraping, Other Formats (see web-scraping) seam bias, Other Sources of Bias, Seam Bias search referral data example, Search Referral Example–Search Referral Example sentiment classification, Weotta, Getting Reviews–Sentiment Classification, Training a Classifier–Training a Classifier, Validating the Classifier–Validating the Classifier, Designing with Data, Lessons Learned training classifier for, Training a Classifier–Training a Classifier, Lessons Learned using results of, Weotta, Designing with Data validating classifier for, Validating the Classifier–Validating the Classifier SIPP (Survey of Income and Program Participation) data example, Imputation Bias: General Issues, Proxy Reporting social media, Can You Get That?

On the Edge: The Art of Risking Everything
by Nate Silver
Published 12 Aug 2024

Secular stagnation: As originally defined, a prolonged period of little to no economic growth, often accompanied by low interest rates. Informally, the sense that technological and economic progress isn’t happening as fast as it should be and that society faces many headwinds. Selection bias: The tendency for members of a population with certain traits to be weeded out, resulting in a biased sample. For instance, the population of NFL quarterbacks suffers from selection bias with respect to arm strength because quarterbacks below a certain threshold will never make the pros. See also: survivorship bias. Semantic: Interpretation, meaning, and context in the study of language.

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And in an earlier hand against Adelstein, Lew called a $10,000 bet on the turn with a jack-high flush draw even though Adelstein had already made a full house and she was drawing dead—the term poker players use for when you literally have zero chance of winning. She also nearly called Adelstein again on the river, telling him she thought he was bluffing and saying, “I’ll get you again.” Indeed, she did. It was the next time she faced a huge bet from Adelstein that she pulled the trigger with J4. *22 Of course, this could be selection bias: we only notice the most egregious cheating cases. Be careful when you play poker. Live tournaments or cash games in highly regulated casinos or cardrooms are by far the safest environment. *23 There was a hand between her and Chavez where they played conspicuously timidly against each other—a “softplay” that sometimes happens when players have a financial or personal relationship and don’t want to risk their chips against each other—but this is considered a venial sin in a cash game.

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See Bankman-Fried, Sam scaling (AI), 496 scalp (sports betting), 516n Scarborough, Joe, 179 Schelling, Thomas, 58, 327–28, 331, 426, 498 Schemion, Ole, 70–72 Scholes, Myron, 479 Schüll, Natasha, 154–55, 162, 165, 166, 167–68, 169, 321 Schwartz, David, 139, 140, 156 science fiction, 453–55 scientific research, 219, 223–24, 228–29, 239–40, 242 secular stagnation, 459–60, 464–67, 465, 496 Seidel, Erik, 44–45, 70, 73, 98, 104, 239 Seiver, Scott, 101, 102–3, 223, 225 Selbst, Vanessa, 67–72, 75, 239, 241, 336, 509n selection bias, 496 self-confidence, 222–23 self-made wealth, 275–76 semantics, 496 semi-bluff, 496 Sense of the Enemy, A (Shore), 224 “Sequences, The” (Yudkowsky), 352–53 set (poker), 496 Shannon, Claude, 396 “shape of things to come,” 496 sharks (poker), 485 sharp (gambling), 496 Shaw, George Bernard, 247 Shear, Emmett, 408, 415, 450, 459–60, 538n shitcoin, 315, 316–18, 496 Shockley, William, 256–57 Shor, David, 268–69, 271 Shore, Zachary, 224, 498 shove (poker), 496 showdown (poker), 42, 496 Shut Up and Multiply, 496 Siegel, Bugsy, 142 Signal and the Noise, The (Silver), 15, 24, 60, 192, 236, 253, 263, 264, 353, 361, 373, 432, 432, 439 signal-to-noise ratio, 496 Silicon Valley concentration of capital in, 255–56, 256 contrarianism and, 25, 254, 414 culture wars and, 272–73 defined, 255–56n, 497 determinism and, 253–55, 297 disruptiveness and, 258, 269–70 employees, 272, 273 essential traits of, 257–62 existential questions and, 297 fox/hedgehog model and, 263–66, 264, 271–72, 414 history of, 256–58, 265 independence and, 239, 268, 273 “move fast and break things” and, 250, 270, 419, 490 narcissism and, 274–75 Obama election and, 267 politics and, 267, 272 public confidence in, 456–57 regulation of, 269–70, 272 River-Village conflict and, 26, 267–75, 290, 295, 505n small disrupter myth, 269–70 techno-optimism and, 249, 250–51, 270, 296, 459, 498 venture capital vs. founders, 251–52, 255, 263–66, 264 See also AI; founders; venture capital Silver, Adam, 198n Silver, Gladys, 11–12 Silver, Jacob, 12 Sinatra, Frank, 146 Singer, Peter, 355 drowning child parable, 357, 358, 359, 368, 483 futurism and, 380 impartiality and, 358–59, 533n, 538n overfitting/underfitting and, 362, 363, 366 positive impact of, 357–58 radicalism of, 356 utilitarianism and, 359, 378, 408 singularity, 497 skill games, 497 See also poker; sports betting “skin in the game,” 497 Slim, Amarillo, 41 slot barns, 497 slots addictive nature of, 164–65, 166, 167, 168–69 advantage play, 158–61 casino design and, 162–63, 167–68 casino revenue and, 163–64, 163 flow state and, 165, 166–68 locals market and, 145 payout structures, 154–56, 155, 156 probabilistic thinking and, 153–55, 155 slowplay (poker), 497 small ball (poker), 497 small-world problem, 497 smart contracts (crypto), 324–25, 484, 497 See also NFTs Smith, Ben, 25 Smith, Dan “Cowboy,” 113–14, 362–63 social media, 30, 261, 321, 330–31n societal institutions, 250, 456–57, 472 solvers (poker), 62 bluffing and, 74, 75, 78, 509n Doyle Brunson and, 43 defined, 497 exploitative strategies and, 78–79 game theory and, 22, 60–61, 62–65, 71, 74 Garrett-Robbi hand and, 125 invention of, 60–61 optionality and, 76–77 space travel, 218–19, 223, 226n, 229, 234–35, 236–37 splash zone, 497 sports See also sports betting; specific sports sports betting adaptability and, 235–36 advertising for, 197–200 AI and, 175–76 analytics and, 171, 191 angles, 192–94, 235–36, 478 arbitrage and, 172–74, 206, 489, 516n, 517n bearding, 207–8, 479 Bid-Ask spread and, 444 bookmakers, 480 bottom-up vs. top-down, 170–71, 192, 516n casino business and, 174–75, 177–78, 182–83, 185–87 cheating, 136 closing line value, 205–6, 206, 481 computer applications and, 172 contrarianism and, 240 deception and, 206–7 fantasy sports, 198, 199, 483 game theory and, 171 getting (money) down, 48, 204–9 inside information and, 177, 187n, 194, 197n, 212–13 Kelly criterion and, 397, 399 key skills for, 191–97 lessons from, 209–14, 520n line shopping, 202–4, 488 manual trading, 175, 176–77 market makers vs. retail bookmakers, 186–90, 187, 489, 518n models and, 179–80, 182 Nash equilibrium in, 58–60, 508n networking and, 191, 197 obsession and, 196 online legalization, 184–85, 198–99n patience and, 259 probabilistic thinking and, 16–17 process-oriented thinking and, 180 profitability of, 175, 178–83, 181, 517n prop bets, 180, 182–83, 494 risk tolerance and, 179, 196 scalping, 516n scandals in, 173, 177 sportsbook menu guide, 183–84 steam chasing, 205, 205–6n, 206, 497 taxation of, 184, 517n vig/vigorish, 172n, 184, 500 See also limits (sports betting) stability-instability paradox, 425, 497 stack (poker), 497 stake, 497 standard deviation, 497 Stanovich, Keith, 24 Stapleton, Joe “Stapes,” 509n statistical significance, 497 statistics analytics and, 23 correlation, 482 moral philosophy and, 360–61 See also analytics; probabilistic thinking steam chasing, 49, 205, 205–6n, 206 Steck, Ueli, 227 steelman arguments, 27, 497 Stewart, Kelly, 199 Stimson, Henry, 420 Stokes, Jon, 434 St.

The Alignment Problem: Machine Learning and Human Values
by Brian Christian
Published 5 Oct 2020

Any preexisting disparity in policing between two otherwise similar neighborhoods is likely only to grow. As Lum and Isaac put it, “The model becomes increasingly confident that the locations most likely to experience further criminal activity are exactly the locations they had previously believed to be high in crime: selection bias meets confirmation bias.”75 The system begins to sculpt the very reality it is meant to predict. This feedback loop, in turn, further biases its training data. Lum and Isaac conclude not only that “drug crimes known to police are not a representative sample of all drug crimes” but, what’s more, “rather than correcting for the apparent biases in the police data, the model reinforces these biases.

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For more on open category learning, see, e.g., Scheirer et al., “Toward Open Set Recognition”; Da, Yu, and Zhou, “Learning with Augmented Class by Exploiting Unlabeled Data”; Bendale and Boult, “Towards Open World Recognition”; Steinhardt and Liang, “Unsupervised Risk Estimation Using Only Conditional Independence Structure”; Yu et al., “Open-Category Classification by Adversarial Sample Generation”; and Rudd et al., “The Extreme Value Machine.” Other related approaches to adversarial examples and robust classification include Liu and Ziebart, “Robust Classification Under Sample Selection Bias,” and Li and Li, “Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics.” For more recent results by Dietterich and his collaborators, see Liu et al., “Can We Achieve Open Category Detection with Guarantees?,” and Liu et al., “Open Category Detection with PAC Guarantees,” as well as Hendrycks, Mazeika, and Dietterich, “Deep Anomaly Detection with Outlier Exposure.”

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“Refining Clinical Risk Stratification for Predicting Stroke and Thromboembolism in Atrial Fibrillation Using a Novel Risk Factor–Based Approach: The Euro Heart Survey on Atrial Fibrillation.” Chest 137, no. 2 (2010): 263–72. Lipsey, Richard G., and Kelvin Lancaster. “The General Theory of Second Best.” Review of Economic Studies 24, no. 1 (1956): 11–32. Liu, Anqi, and Brian Ziebart. “Robust Classification Under Sample Selection Bias.” In Advances in Neural Information Processing Systems, 37–45, 2014. Liu, Lydia T., Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt. “Delayed Impact of Fair Machine Learning.” In Proceedings of the 35th International Conference on Machine Learning, 2018. Liu, Si, Risheek Garrepalli, Thomas G.

The New Science of Asset Allocation: Risk Management in a Multi-Asset World
by Thomas Schneeweis , Garry B. Crowder and Hossein Kazemi
Published 8 Mar 2010

The often lower returns of these funds are not contained in the live portion of most databases and one must ask for the dead fund databases in order to measure the actual returns to investment in funds that may have existed in the past. Other biases may also exist in any single database, such as selection bias (databases differ on their requirements for reporting) and reporting bias (managers may be in one strategy but report as in another). The extent of these biases may differ by strategy, time period, and database. Thus, proper due diligence must be used in understanding the actual performance characteristics of a fund before considering investment.

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See Modern Portfolio Theory (MPT) MSCI Emerging Markets, 257, 258 Multi-asset allocation, 70–71, 133 performance, 148, 153, 158 principal concerns, 167 Multi-factor model, 41, 42, 43, 44, 50–54 Multi-factor regression model, 51 Multivariate linear regression, 198 Mutual funds, 129 NAREIT Domestic Real Estate Index, 179 NASDAQ, 250, 251 NCREIF National Property Index (NPI), 173, 175 Non-linear payoffs, 197 Old Portfolio Theory (OPT), 3 Operational risk, 197 Opportunity cost risk, 210 Optimization models, 92–98, 200 Options, 11, 197 and return distribution, 27 and risk management, 206 trading, 12 Option theory, 10–11, 231, 238, 246 Order, definition of, 242–245 Oversight, 243–244 Parameter estimation error, 25 Peer group creation, 126–132 Performance alpha, 46–47 Performance attribution, 34 Performance evaluation, conditional, 53–54 Political risk, 197 Portfolio insurance strategy, 92, 107 Portfolio management: active versus passive, 46–47 convexity of returns, 49–50 market segment weightings, 70–71 performance comparison between aggressive and conservative, 72–73 risk based, 66 Portfolio returns: differences in aggressive and conservative management, 86–87 ranked by BarCap US Aggregate, 83 ranked by S&P 500, 81 weightings, 84–85 Portfolio risk, 11, 30–34 Portfolios: bootstrapped or resampled, 94 measuring exposure, 198 model, 102 quantitative construction models, 93 rebalancing, 15, 107, 201 return volatility, 99 292 Portfolios (Continued) risk based, 106 risk decomposition of, 202–203 tactical asset, 106 Portfolio weights, 94 Price risk, 33, 219 Private equity, 61, 65, 118, 148–153 benchmarks, 170–173, 174, 275 performance of indices, 171 return and risk performance, 151–153 returns ranked by S&P 500, 154, 172 sources of return, 151 Probability, 2, 20 Product development, 15–16 Pro forma performance, 167, 169 Protected investment strategy, 107 Pure security, 62 Purity, style, 126–132 Put-Call Parity Model, 11 Put option, 11 Put protected investment strategy, 107 Quantitative model, 102–103 Rate of return: annualized S&P 500, 93 excess break-even rate, 43–44 and risk, 197–198 Real estate, 61, 65, 129, 153–160, 176 benchmarks, 173–179, 274 comparison benchmark performance, 157 correlation with S&P 500 indices, 178, 181 international and FTSE returns, 177 prices, 155–156 return and risk performance, 156–158 sources of return, 155–156 Real estate investment companies (REITs), 155 Reallocation strategy, 103 Reasonable man theory, 60 Rebalancing, 15, 107, 201 Regression coefficients, 52 Regression model, 51, 52 Regulations, governmental, 127, 197, 244–245, 247–248 REITs (real estate investment companies), 155, 157–158, 179 Replication-based indices, 122–126, 223 Reporting bias, 192 INDEX Return and risk: of asset classes through business cycles, 251–270 characteristics of alternative investments, 61–62 characteristics of indices, 169 commodities, 162–163 differences among similar asset class benchmarks, 167–194 hedge funds, 140–143 historical attributes and strategy allocation, 66–70 historical comparisons, 71–74 managed futures, 146–148 models post-1980, 11–13 multi-factor estimation, 50–54 performance results, 66 predictability of, 95–96 private equity, 151–153 real estate, 156–158 sources in alternative investments, 134–166 and strategic asset allocation, 99 Return estimation models, 50–54 Return generating models, 46 Return intervals, 36 Returns: benchmark, 136 CISDM hedge funds, 145 commodities, 160–162 hedge funds, 139–140 and managed futures, 146 and performance of investment managers, 215 real estate, 155–156 Return to risk, 2, 18, 19 Return volatility, 61, 99 Risk, 1–2 absolute, 65 and alternative investments, 134–166 assessment, 28–30 aversion, 98, 100, 133 budgeting and asset allocation, 195–211 decomposition, 34, 202–203 determinants, 23 and expected return, 230 factor weightings, 54–55 individual vs. portfolio, 69 and liabilities, 96 management of (See Risk management) 293 Index measurements of, 20–38, 96–97 measures of exposure, 63 qualitative, 63 and rate of return, 197–198 tolerance, 89, 117–119 and tracking error, 97–98 what it is, 22–24 Risk factor sensitivity analysis, 34 Risk-free assets, 4 Risk hedge ratio, 204 Riskless rate of interest, 7 Risk management, 1–2, 3, 11, 107, 214, 247 benefits of reduction, 74 goal of, 199 models, 247 multi-factor approach to, 195–200 using futures, 203–206 using options, 206 and volatility, 200–202 Risk premia, 7, 214 Robertson, Julian, 222 Rogers International Commodity Index (RICI), 182 Roll returns, 166 Rosenberg, Barr, 10 Russell Growth, 120, 207, 251 volatility, 254, 255, 256, 257 Salomon Brothers Bond Indices, 168 Sample selection, 38 Satellite allocation, 110–133 Scholes, Myron, 11 Securities: fixed income, 12, 24, 63, 65, 272 investable, 123 pure, 62 Securities and Exchange Commission (SEC), 229 Securitization, 155 Security market line, 6 Seed capital, 153 Selection bias, 192 Semi-standard deviation, 97 Semi-variance, 30 Sensitivity, market, 74–82, 82–84, 89 Sharpe Ratio, 18, 26–28, 37, 43 Skewness, 29, 62, 97, 223 Slope of the yield curve, 101 Small minus big (SMB) factor, 45 Smoothing, 28, 175 S&P 500, 36, 37, 185 annualized rate of return, 93, 168 annualized standard deviation, 93 benchmark returns, 79, 138 CISDM CTA indices, 150 commodity benchmarks, 165 correlations with real estate indices, 175, 178, 181 correlation with Barclays Capital U.S.

All About Asset Allocation, Second Edition
by Richard Ferri
Published 11 Jul 2010

Other monitoring companies use flawed data collection methodologies. For instance, they do not include the performance of funds that have closed or merged. This produces an upward survivorship bias in the hedge fund indexes. When a hedge fund has a bad quarter, the managers may simply choose not to report the results. This leads to a selection bias in the index performance. Most monitoring companies allow a newly reporting fund to “backfill” performance with simulated historic returns that no investor actually earned. That creates a backfill bias in the indexes. Finally, most monitoring companies allow the hedge fund managers to price their own illiquid securities, thus introducing a pricing bias into the indexes.

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INDEX A Actively managed funds, 21, 97 Advisors, 8, 14–15, 313–315 Alternative investments, 189–215, 214t collectibles, 211–214, 212f, 213f commodities, 191–206, 193f, 195f, 201f, 202f, 204t hedge funds, 206–211, 208t list of, 214–215 American Depositary Receipts (ADRs), 129 Asset allocation, ix–xi, 41–64, 63f correlation analysis, 47–53, 57–61 fallibility of, 62–64 history of, 41–44 rebalancing, 44–47 risk and return, 53–57 strategies for, xiv–xv two-asset-class model for, 53 (See also specific topics) Asset allocation stress test, 278–284, 281t, 283t Asset classes, 18–19 with low or varying correlation, 95–97 with low-cost availability, 97–98 range of volatility in, 34–35 with real expected return, 94 REITs as, 175–179 (See also Multi-asset-class investing; specific classes) B Backfill bias, 209 Basis points, 77 Bear markets, 276–277, 294–295 Behavioral finance, 271–289 asset allocation stress test, 278–284, 281t, 283t bear markets, 276–277 observations from, 273–275 personal risk tolerance, 275 rebalancing risk, 284–285 risk avoidance, 285–286, 286t risk tolerance questionnaires, 277–278, 287–289 Beta, 116 Bond market, global, 148–149, 163 Bonds, 19, 89, 90, 148–156 corporate, 72–75 credit risk with, 152–155 emerging market, 164–165 forecasting returns, 228–229, 238–240 high-yield corporate bonds, 157–159 investment-grade, 152–157 maturity structure, 151–152 tax-exempt municipal, 148, 165–166 U.S., 88–89 “your age in bonds,” 243–244, 266–270, 295–296 (See also specific bond types) Bulletin-board stocks, 104, 104t C Canadian stocks, 138–139 Cash/cash-type investments, 10 Changing allocation, 291–299 guidelines for, 298–299 just before retirement, 294–295 and periodic market data, 296–297, 297f reasons for, 292 when goals are within reach, 293–294, 293f when investing for others, 295–296 Collectibles, 211–214, 212f, 213f Commercial real estate investments, 173–175 331 332 Commodities, 30, 189–195, 193f, 195f, 200–206, 201f, 202f, 204t, 224 indexes for, 194–195, 200–201 in portfolios, 201–203 real return on, 94 and supply and demand, 192–194 Commodity funds, 89 Commodity futures, 196–201, 197f, 200f, 203 Computer simulations, 81–82 Corporate bonds, 72–75, 151, 157–159, 229t, 230f Correlation, 47–53, 51t inconsistency of, 57–61, 95 low or varying, 95–97 measuring, 50 negative and positive, 48, 95 for real estate investments, 179–183 with U.S. stocks and bonds, 89 in well-diversified portfolios, 62 Costs of investing (see Fees and costs) Credit risk, 152–155, 153t, 154f, 155f Currency risk, 128–129, 128f, 133t D Default risk (bonds), 158–159 Deflation, 239 Developed markets, 130, 163 Developed-market indexes, 132–134 Diversification, 41, 43f, 55f, 59f, 60f, 60t, 62, 90 within funds, 97 with microcap stocks, 110, 113 rebalancing for, 45 with small-cap value stocks, 121–125 (See also Multi-asset-class investing) Dividends, 235–238 Dollar cost averaging, 309–311 E EAFE Index, 132–138, 133t, 134f, 136f, 138t, 140f Early savers, 244, 247–251, 250f, 250t, 251t Economic factor forecasting, 233–235 Efficient frontier, 54, 54f, 58–59, 66, 123, 124 Index Efficient market theory (EMT), 43 Emerging markets, 98–99, 130–131, 139–141, 140f, 140t, 141f, 163 Equity REITs, 176–183 Exchange-traded funds (ETFs), 311–313 of alternative assets, 214 capital gains on, 305–306 costs and fees with, 303, 304 for global diversification, 99 international, 144 in investment plan, 11–13, 21–22 low cost of, 97 Expectations for returns, 219 (See also Forecasting) F Factor performance analysis: in forecasting, 233–235 international equities, 142–143 U.S. equities, 109–121 Fad investing, 13–14 Fear of regret, 274–275 Federal Reserve, 235 Fees and costs, 301–315, 302t comparing fund expenses, 303–305 cost of taxation, 305–308 index funds and ETFs, 304f, 311–313 low-fee advisors, 313–315 and performance, 302–303 and tax swaps, 308–311 Fixed-income investments, 147–169, 150f, 156t, 167f, 167t, 168t, 223f bond market structure, 148–149 corporate bonds, 72–75 credit risk, 152–155 example of, 166–167 forecasting returns, 238–240 foreign market debt, 163–165 high-yield corporate bonds, 157–159 investment-grade bonds, 155–157 list of, 167–168 maturity structure, 151–152 risk and return with, 149–151 tax-exempt municipal bonds, 165–166 TIPS, 28, 29, 159–163 (See also specific investments) Index Forecasting, 13, 219–242, 234f, 236f, 241t creating forecasts, 240–241 and dividends, 235–238 economic factor, 233–235 Federal Reserve and GDP growth, 235 fixed income returns, 238–240 and inflation, 225–226 market returns, 220–221 risk-adjusted returns, 221–225 stacking risk premiums, 226–232 Foreign market debt, 163–165 Foreign stocks (see International equity investments) Frontier markets, 132 Fund expenses, 303–305 Fundamental differences, 90–93 G Global markets, 98–99, 98f, 129–132, 131f, 135f, 137f Government bonds, 151, 164f, 165f (See also Treasury bonds) Gross domestic product (GDP), 234f, 235 Growth stocks, 108, 114–116, 119–121 H Hedge funds, 190–191, 206–211, 208t High-yield corporate bonds, 157–159, 158f Home ownership, 173, 183–186, 259 I Index funds, 21–22, 304f, 311–313 commodities, 203–206 costs and fees with, 303–304 for global diversification, 99 low cost of, 97 U.S. equity, 125–126 value vs. growth, 92–93 Indexes, 106, 116–121, 118t, 119f, 120f bond, 155–157, 163–164 collectibles, 212–214 commodities, 194–195, 200–201 developed-market, 132–134 EAFE, 132–138 333 emerging market, 139–141 hedge fund, 209–210 international, 68–70 microcap, 111–113 midcap, 111 REIT, 177–178, 180–182 U.S. equities, 105 Inflation, 103, 221 and forecasting, 225–226 and interest rates, 238–240, 239f and real expected return, 94 and rental properties, 173 Inflation-protected securities, 28, 29, 162–163 [See also Treasury Inflation-Protected Securities (TIPS)] International equity investments, 127–145, 142t, 144t allocation of, 137–138, 143 Canadian stocks, 138–139 currency risk, 128–129, 128f, 133t developed-market indexes, 132–134 EAFE Index, 132–138, 133t, 134f, 136f, 138t, 140f emerging markets, 139–141, 140f, 140t, 141f global markets, 129–132, 131f, 135f, 137f list of, 143–144 in multi-asset-class investing, 68–72 size and value factors, 142–143 Investment plan, 3–23 academics’ views of, 19–20 asset allocation in, 15–16 asset classes in, 18–19 avoiding bad advice, 14–15 characteristics of, 4–6 and fad investing, 13–14 monitoring and adjusting, 16–18 mutual funds and ETFs in, 11–13 and overanalysis of market data, 22 and professional advice, 8 selection of investments, 21–22 and shortcuts, 6–7 types of assets in, 9–11 Investment policy statement (IPS), xiii, 5 334 Investment pyramid, 9–11, 9f, 245–247, 246f Investment risk, 25–39 defining, 29–31 and myth of risk-free investments, 26–29 as running out of money in retirement, 31–32 volatility as, 32–38 Investment styles, 19 Investment-grade bonds, 152–157 L Large-cap stocks, 107–109, 117t, 119 Large-cap style indexes, 117 Liability matching, 253–254 Life-cycle investing, 17–18, 243–270 early savers, 247–251, 250f, 250t, 251t investment pyramid in, 245–247, 246f and life phases, 244–245 mature retirees, 263–266, 265f, 265t, 266t midlife accumulators, 252–256, 255f, 255t, 256t modified “your age in bonds” for, 266–270 transitional retirees, 256–262, 261f, 262t Limited partnerships (real estate), 174 Long-term investments, 10 Low-cost asset classes, 97–98 Low-fee advisors, 313–315 M Market data, 22, 296–297 Market returns, forecasting, 220–221 Market risk factor, 116, 272 Markets: bear, 276–277, 294–295 bond, 148–149 developed-market indexes, 132–134 and dividends, 235–238 emerging, 139–141 foreign market debt, 163–165 global, 98–99, 129–132 Index overanalysis of market data, 22 periodic market data, 296–297 stock, 103–105 (See also specific markets) Markowitz, Harry, 41–43 Mature retirees, 244–245, 263–266, 265f, 265t, 266t Microcap stocks, 107–113, 110t, 111f, 124f Midcap stocks, 107–109, 111–113, 111f Midlife accumulators, 244, 252–256, 255f, 255t, 256t Modern portfolio theory (MPT), viii, 43–44, 79, 171, 189, 271 Morningstar classifications, 106–109, 107f, 108t, 109f Morningstar ratings, 14 Multi-asset-class investing, 65–83, 67f corporate bonds, 72–75, 73t, 74f example of, 75–79, 76f, 76t, 78–79f for expanding the envelope, 66–67 international stocks, 68–72, 69t, 70f, 71f Municipal bonds, tax-exempt, 148, 165–166 Mutual funds, 30, 92f, 93f, 148 of alternative assets, 214 capital gains on, 305–306 commodities, 203–206 costs and fees with, 303–305 emerging market, 131 global equity, 130 high-yield bonds, 159 international, 144 in investment plan, 11–13 in late 1990s, 92–93 low-cost fixed-income, 167–168 no-load actively managed, 97 REIT, 174, 175, 187 swapping, 308–309 (See also Index funds) N Nasdaq, 103, 104, 104t New York Stock Exchange (NYSE), 103, 104, 104t No-load actively managed funds, 97 Index Noncorrelation, 48–53, 51f Northwest quadrant, 54, 55f, 66, 80 P Passive funds, 21 Pension plans, 30, 258–259 Performance: factor performance analysis, 109–121 and fees, 302–303 and future results, 14 and investment cost, 302–303 long-term, 16 (See also Forecasting; Returns) Portfolio building (see Investment plan; Life-cycle investing) Portfolio risk, 26, 275 Price-to-earnings (P/E) ratio, 236–238, 237f Pricing bias, 209 Primary market, 103 Professional advisor(s), 8, 14–15, 313–315 R Real estate investment trusts (REITs), 174–182f, 186–187, 187t Real estate investments, 171–187, 172t commercial, 173–175 correlation analysis, 179–183 home ownership, 183–186, 259 list of, 186–187 REITs, 174–182f, 186–187, 187t Real return, 161 on commodities, 94 on U.S. stocks and bonds, 102–103 Rebalancing, 44–47, 46t, 55, 59, 67, 284–285 Regression to the mean, 45 Retirement: bear markets just before, 294–295 and life-cycle investing, 256–266 running out of money in, 31–32 Returns, 35–38, 35t, 56t, 222t and asset allocation, 20 expectations for (see Forecasting) fixed-income, 149–151, 238–240 on international investments, 68–70 335 market, 220–221 with multi-asset-class investing, 75–77 real, 94, 161 on real estate investments, 171–173 on REITs, 180–183 and risk, 35, 53–57, 61f, 223f risk-adjusted, 221–225, 221t on U.S. equity investments, 102–103, 102t Risk: credit, 152–155, 153t, 154f, 155f currency, 128–129, 128f, 133t default, 158–159 with fixed-income investments, 149–151 investment, 25–39 perceived, 26 rebalancing, 284–285 with REITs, 180–183 and return, 35, 53–57, 61f, 221–225, 223f with small-cap value stocks, 121–125 volatility as, 32–38 Risk avoidance, 285–286, 286t Risk diversification, 90, 121–125 Risk premiums, stacking, 226–232, 232t Risk tolerance, 17, 275 Risk tolerance questionnaires, 16–17, 277–278, 287–289 Risk-adjusted returns, 221–225, 221t Risk-free investments, myth of, 26–29 Rolling correlations, 58f, 96, 96f S Secondary market, 103 Selecting investments, 21–22, 87–100 four-step process for, 88 with fundamental differences, 90–93 in global markets, 98–99 guidelines for, 89–98 with low or varying correlation, 95–97 with low-cost availability, 97–98 with real expected return, 94 U.S. stocks and bonds, 88–89 Index 336 Selection bias, 209 Size factor: international equity investments, 142–143 U.S. equity investments, 106, 107 Size risk factor, 116 Small-cap stocks, 107–109, 118t, 121–125, 122t, 123f, 124f, 230–231, 231f Small-cap style indexes, 117–118 Social Security, 10, 11, 258–259 Speculative capital, 11 Stacking risk premiums, 226–232, 232t Standard deviation, 33–38, 34f, 37t, 38t Stock markets, 105 1987 crash, 30–31 in 1990s, 276 in 2007–2009, 276–277 during crises, 89 Stocks, 19, 89–90, 229–230 Canadian, 138–139 international, 68–72 (See also International equity investments) small-cap value, 121–125 U.S., 88–89 (See also U.S. equity investments) Style factor, 107–109 Survivorship bias, 209 T Tax swaps, 308–311, 309f, 310t Tax-deferred accounts, 306–307 Taxes, 19 and after-inflation returns, 225–226 on bonds, 165–166 on commodity funds, 205–206 as investment expense, 305–308 on T-bill returns, 27–28 Tax-exempt municipal bonds, 148, 165–166 Total risk, xi–xii, 43f Transitional retirees, 244, 256–262, 261f, 262t Treasury bills (T-bills), 26–28, 27f, 28f, 151–152, 152f, 225–227, 226f Treasury bonds, 72–75, 151–152, 160–163, 161f, 229t Treasury iBonds, 162–163 Treasury Inflation-Protected Securities (TIPS), 28, 29, 156, 159–163, 161f, 162f, 227–228, 228f, 240 Treasury notes, 152f Two-asset-class model, 53 U Unit investment trusts (IUTs), 97 U.S. bond investments, 88–89 U.S. equity investments, 101–126, 125t, 140f, 141f, 230f and broad stock market, 105 and currency risk, 128–129 factor performance analysis, 109–121 history of returns on, 102–103 list of, 125–126 and market structure, 103–104 Morningstar classification methods, 106–109 selecting, 88–89 sizes and styles of, 106–109 small-cap value and risk diversification, 121–125 V Value risk factor, 116, 142–143 Value stocks, 108, 114–116, 119–125, 231f Volatility, 222, 224, 225f of commodity prices, 94 of foreign stocks, 127–128 of international stocks, 71, 72 as investment risk, 29, 32–38 measuring, 32–34, 33f, 34f price, 29–30 Y Yield spread, 74 “Your age in bonds” approach, 243–244, 266–270, 295–296 

How to Read a Paper: The Basics of Evidence-Based Medicine
by Trisha Greenhalgh
Published 18 Nov 2010

A more objective analysis showed that whilst one of two did indeed suggest an effect, a true estimate based on all the available studies suggested that vitamin C had no effect at all on the course of the common cold. Pauling probably did not deliberately intend to deceive his readers, but because his enthusiasm for his espoused cause outweighed his scientific objectivity, he was unaware of the selection bias influencing his choice of papers. Evidence shows that if you or I were to attempt what Pauling did—that is, hunt through the medical literature for ‘evidence’ to support our pet theory—we would make an equally idiosyncratic and unscientific job of it [3]. Some advantages of the systematic review are given in Box 9.1.

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Index absolute risk reduction (ARR) absolutism absorptive capacity (organisations) academic detailing accessible standards ‘accountability culture’ accuracy ACP PIER additional risk adult learning advertising, DTCA advice for patients AGREE instrument allocation concealment, CONSORT checklist analysis of variance anecdotes DTCA anti-inflammatory drugs, non-steroidal anticoagulant therapy applicability clinical guidelines appraisal, critical, see critical appraisal ARR (absolute risk reduction) aspirin, meta-analyses assessment ‘blind’ clinical guidelines methodological quality needs assumptions, unquestioned avoidable suffering baseline data, CONSORT checklist baseline differences behavioural learning bias expectation selection systematic work-up (verification) biological markers of disease ‘blind’ assessment blinding, CONSORT checklist blobbogram, see forest plot bluffing, deliberate boundaries fuzzy organisational break-even point browsing, informal Caesarean section, see induced delivery CardioSource care, quality of care pathways, integrated (critical) case systematic bias case reports case studies ‘caseness’ causation tests for CBT (cognitive behaviour therapy) Centre for Evidence-Based Medicine (CEBM) CHAIN (Contact, Help, Advice and Information Network) ‘champions’ cheating with statistical tests checklist CONSORT context-sensitive QADAS systematic reviews data sources choice, informed cholesterol hypercholesterolaemia Cinderella conditions citation chaining classical management theory clinical applicability clinical decision-making clinical disagreement clinical evidence clinical freedom clinical guidelines implementation clinical heterogeneity clinical prediction rules ‘clinical queries’ clinical questions clinical trials non-randomised controlled RCT, see randomised controlled trials CME (continuing medical education) Cochrane, Archie Cochrane Collaboration Cochrane EPOC, see EPOC group cognitive behaviour therapy (CBT) cohort studies systematic bias collection of data collective knowledge common sense comparable groups COMPASEN format completeness of follow-up complex interventions complexity theory confidence intervals diagnostic tests conflict of interest consistency CONSORT statement RCTs Contact, Help, Advice and Information Network (CHAIN) context context-sensitive checklist context-specific psychological antecedents quality improvement case studies receptive context for change continuing medical education (CME) continuous results control group controlled clinical trials, non-randomised controlled trials, randomised, see randomised controlled trials correlation correlation coefficient Pearson cost analysis cost cost-minimisation ‘cost per case’ counting-and-measuring perspective covariables criteria, stringent critical appraisal pre-appraised sources qualitative papers critical care pathways cross-sectional surveys cumulative meta-analyses current practice cut-off point DALY (disability-adjusted life year) data baseline collection dredging paired pooled skewed databases DARE EPOC primary studies systematic reviews TRIP see also sources, resources decision-making evidence-based evidence-based practice shared therapy deduction deep venous thrombosis (DVT) deliberate bluffing delivery, induced design complex interventions RCT research studies ‘detailers’ detailing, academic diabetes qualitative research shared decision-making yoga control diagnosis diagnostic sequence diagnostic tests validation ‘dice therapy’ dichotomy qualitative direct costs direct-to-consumer-advertising (DTCA) disability-adjusted life year (DALY) disagreement, clinical discourse analysis ‘doing nothing’ Donald, Anna ‘dose dredging, data ‘drug reps’ drug treatments drugs, see also therapy, treatments duration of follow-up DVT (deep venous thrombosis) DynaMed EBM, see evidence-based medicine economic analyses editorial independence education for patients educational intervention, specific effective searching efficacy analysis eligibility criteria embodied knowledge endpoints, surrogate epilepsy EPOC Group ethical considerations drug trials QALYs RCTs ethnography Evans, Grimley evidence application on patients formalisation hierarchy of level of ‘methodologically robust’ evidence-based decision-making evidence-based guidelines evidence-based medicine (EBM) criticisms essential steps reading papers web-based resources ‘evidence-based organisation’ evidence-based policymaking evidence-based practice expectation bias ‘expert opinion’ harmful practices explanation of results surrogate endpoints explanatory variables explicit methods explicit standards external validity ‘eXtra’ material Eysenck, Hans F-test falsifiable hypotheses federated search engines ‘female hypoactive sexual desire’ focus groups focusing, progressive follow-up forest plot formalisation of evidence formulation of problems freedom, clinical fuzzy boundaries ‘geeks’ general health questionnaire, SF-36 general psychological antecedents generalisability CONSORT checklist GIDEON (Global Infectious Diseases and Epidemiology Network) GIGO (garbage in, garbage out) GOBSAT (good old boys sat around a table) ‘gold standard’ test good clinical questions Google Scholar Grimshaw, Jeremy Grol, Richard group relations theory groups comparable focus subgroups guidelines as formalised evidence implementation practice SQUIRE guiding principles Guyatt, Gordon hands-on information hanging comparative harmful practices ‘expert opinion’ health professionals evidence-based practice shared decision-making health-related lifestyle Helman, Cecil ‘here and now’ heterogeneity hierarchy of evidence pharmaceutical industry traditional histogram holistic perspective human factor human resources HYE (Healthy Years Equivalent) hypercholesterolaemia ‘hypoactive sexual desire’, female hypothesis, null ‘illness scripts’ implementation clinical guidelines guidelines IMRAD format inadequate optimisation inception cohort incremental cost independence, editorial indirect costs individualised approaches induced delivery inductive reasoning industry, pharmaceutical, see pharmaceutical industry infertility informal browsing information ‘jungle’ information needs informed choice ‘informed consent’ intangible costs integrated care pathways ‘integrated’ EBM teaching inter-rater reliability internet-accessible format interventions complex CONSORT checklist cost analysis effect of meta-analyses organisational simple specific educational interview qualitative research see also questionnaire invited review items (questionnaire) iterative approach journalistic review ‘jungle’, information Kappa score knowledge, collective knowledge managers laboratory experiments learning organisation least-squares methods ‘length of stay’ level of evidence lifestyle, health-related likelihood ratio nomogram literature searching long-term effects longitudinal survey looking for answers ‘lumpers and splitters’ mammogram management theory, classical Marinker, Marshall marketing masking, see blinding Maskrey, Neal McMaster Health Utilities Index Questionnaire mean inhibitory concentration (MIC) mean (statistical) measurements mechanistic approach mediator/moderator effect medicine evidence-based, see evidence-based medicine ‘narrative-based’ Medline systematic reviews meta-analyses aspirin interventions methodological quality assessment problematic descriptions systematic reviews ‘methodologically robust’ evidence mixed method case study motorcycle maintenance multiple interacting components n of 1 trial ‘narrative-based medicine’ narrative interview NAHA (National Association of Health Authorities and Trusts) National Guideline Clearinghouse needs assessment negative predictive value ‘negative’ trials neonatal respiratory distress syndrome NICE (National Institute for Health and Care Excellence) NNT (number needed to treat) nomogram, likelihood ratio non-diseases non-medical factors non-medical treatments non-normal data, see skewed data non-parametric tests non-randomised controlled clinical trials non-significant results, relevant non-steroidal anti-inflammatory drug (NSAID) normal distribution ‘normal range’ normative orientation Nottingham Health Profile null hypothesis 30 objective of treatment one-stop shopping online material online tutorials, effective searching operational orientation opinion leader opportunity samples, questionnaire research option grids organisation, evidence-based organisational boundaries organisational case studies organisational interventions original studies original study protocol CONSORT checklist OSIRIS patient trial other-language studies outcome measures ‘outcomes research’ outliers p-value paired data papers economic analyses guidelines meta-analyses methodological quality qualitative research quality improvement case studies questionnaire research reading rejection systematic reviews ‘trashing’ participants qualitative research spectrum of patient-reported outcome measures (PROMs) patients advice or education for evidence application patient’s perspective ‘typical’ viewpoint withdrawal from studies Pearson correlation coefficient peer review per-protocol analysis personal digital assistants (PDAs) personal experiences perspective counting-and-measuring holistic patient’s researcher’s pharmaceutical industry evidence-based practice ‘grey literature’ pharmacokinetic measurements pharmacotherapy (PHA), see drug treatments philosophical-normative orientation PIER, see ACP PIER pilot trial piloting, questionnaire research ‘placebo’ effect clinical research studies methodological quality point-of-care resources policymaking evidence-based pooled data populations cohort studies guidelines qualitative research questionnaire research sub- positive predictive value post-test probability postal questionnaire practical-operational orientation practice, evidence-based practice guidelines pre-appraised sources pre-test probability precision prediction rules, clinical preliminary statistical questions prenatal steroid treatment press cutting prevalence primary studies PRISMA statement probability pre-/post-test problem formulation process evaluation professional behaviour prognosis progressive focusing PROMs (patient-reported outcome measures) prostate-specific antigen (PSA) test protocols original study protocol per-protocol analysis protocol-driven approach Psychiatry Online psychological antecedents, context-specific psychometric studies psychometric validity PubMed purposive sample Q-TWIST QADAS (Quality in Diagnostic and Screening tests) checklist QALY (quality-adjusted life year) QOF (Quality and Outcomes Framework) qualitative research quality methodological trial design quality improvement case studies quality improvement cycle ‘quality of care’ quality of life PROMs ‘queries’, clinical questionnaire ‘questionnaire mugger’ questionnaire research SF-36 general health questions good clinical preliminary statistical QUORUM statement quota sampling frame r-value random samples, questionnaire research randomised controlled trials (RCTs) checklist CONSORT statement cumulative meta-analyses hierarchy of evidence systematic bias rating scale measurements reading papers ‘real-life’ circumstances receptive context for change recruitment dates, CONSORT checklist reflexivity regression (statistical) rejection, papers relevant non-significant results reliability, inter-rater reporting format, structured reports, case reproducible tests research design ‘outcomes’ qualitative questionnaire research question researcher’s perspective secondary resources, Point-of-care respiratory distress syndrome, neonatal response rate retrospective subgroup analysis reviews clinical guidelines peer systematic Richard, Cliff risk, additional risk risk difference, see absolute risk reduction role preference safety improvement case studies sample size CONSORT checklist sciatica scientific jargon screening mammogram tests SD (standard deviation) search engines, federated searching effective literature secondary research clinical guidelines selection bias semi-structured interview sensitivity sensitivity analysis sequence generation, CONSORT checklist SF-36 general health questionnaire shared decision-making significance, statistical simple interventions skewed data snowball samples, questionnaire research social cognition social movement ‘social stigma’ ‘soft’ science Someren, Van sources pre-appraised synthesised specialised resources specific educational intervention specificity spectrum of participants ‘splitters and lumpers’ sponsors and stakeholders SQUIRE guidelines stages of change models stakeholders standard current practice standard deviation (SD) standard gamble measurements standardisation standards, explicit and accessible statin therapy statistical questions, preliminary statistical significance statistical tests appropriate evaluation statistics STEP (safety, tolerability, efficacy, price) steroid treatment, prenatal stratified random samples stringent criteria stroke anticoagulants meta-analyses methodological quality structured reporting format studies case cohort design in-/exclusion of participants organisational case original protocol other-language ‘patients’ primary process evaluation psychometric research question (un)original validation withdrawal of patients subgroups, complex interventions retrospective analysis subjective judgements subpopulations surfactant treatment surrogate endpoints surveys cross-sectional literature longitudinal Swinglehurst, Deborah synopses synthesised sources systematic bias systematic reviews databases evaluation evidence-based practice systematically skewed samples t-test table, two-by-two tails target population target variable X2-test tests diagnostic ‘gold standard’ non-parametric PSA reproducible screening statistical theoretical sampling therapy anticoagulant CBT decision-making ‘dice’ NSAID statin see also treatments therapy studies, trial design thrombosis, DVT time trade-off measurements traditional hierarchy of evidence transferable results ‘trashing’ papers Treasury’s viewpoint treatments drug non-medical objective of prenatal steroid see also therapy trials design n of ‘negative’ non-randomised controlled clinical pilot randomised controlled, see randomised controlled trials triangulation TRIP tutorials, online TWIST two-by-two table ‘typical’ patients underfunding ‘unoriginal’ studies unquestioned assumptions validation clinical guidelines diagnostic tests validity external psychometric variables explanatory statistical regression verification bias viewpoint of economic analyses ‘washout’ periods web-based resources, EBM Whole Systems Demonstrator work-up bias WTP/WTA (Willingness to Pay/Accept)

Getting Back to Full Employment: A Better Bargain for Working People
by Dean Baker and Jared Bernstein
Published 14 Nov 2013

San Francisco: Federal Reserve Bank of San Francisco. http://www.frbsf.org/economic-research/files/wp11-05bk.pdf Davis, Steven J., R. Jason Faberman, and John C. Haltiwanger. 2013. “The Establishment-Level Behavior of Vacancies and Hiring.” Quarterly Journal of Economics, Vol. 128, No. 2, pp. 581-622. Doucouliagos, Hristos, and T. D. Stanley. 2009. “Publication Selection Bias in Minimum-Wage Research? A Meta-Regression Analysis.” British Journal of Industrial Relations, Vol. 47, No 2, pp. 406-28. Feldstein, Martin. 1997. “The Costs and Benefits of Going From Low Inflation to Price Stability.” In Christina D. Romer and David H. Romer, eds., Reducing Inflation: Motivation and Strategy (Chicago: University of Chicago Press). http://www.nber.org/chapters/c8883.pdf Fischer, Stanley. 1981.

Rewriting the Rules of the European Economy: An Agenda for Growth and Shared Prosperity
by Joseph E. Stiglitz
Published 28 Jan 2020

All the great American universities are state universities or not-for-profit organizations. At the elementary and secondary school level in the United States, however, independent, not-for-profit charter schools have become fashionable and intensely studied. The evidence reveals that they have not performed any better than public schools, especially when taking selection bias into account (namely, that private schools can choose who attends and turn down any student who might not perform well).11 The interests of private, for-profit schools are aligned neither with those of their students nor with those of society. Instead, they prioritize profit. Parents, especially those who have less formal education themselves, have a hard time judging what makes for a good school.

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David Card and Alan Krueger, “Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania,” The American Economic Review 84, no. 4 (Sept. 1994): 772–93. 6. See the metastudies (quantitative studies of studies) on the United States: Hristos Doucouliagos and T. D. Stanley, “Publication Selection Bias in Minimum-Wage Research? A Meta-Regression Analysis,” British Journal of Industrial Relations (June 2009) and The Effects of a Minimum Wage Increase on Employment and Family Income (Washington, DC: Congressional Budget Office, Feb. 2014). For the UK: Megan de Linde Leonard, T. D. Stanley, and Hristos Doucouliagos, “Does the UK Minimum Wage Reduce Employment?

How to Talk to a Science Denier: Conversations With Flat Earthers, Climate Deniers, and Others Who Defy Reason
by Lee McIntyre
Published 14 Sep 2021

When you try to explore this, however, the Flat Earther makes clear that they are only interested in the fact that Chicago is sometimes visible (which is consistent with their theory that the Earth is flat), and not at all curious about why it is sometimes not visible (which their theory fails to explain). In fact, as we saw, they will reject as fake any credible scientific theory that explains both why the city is sometimes visible and sometimes not, in favor of their own theory, which cannot account for why the city is not always visible. This is a perfect example of the kind of selection bias that is at the heart of cherry-picking evidence, which is deeply rooted in a common cognitive error called confirmation bias.5 With confirmation bias, we are motivated to find facts that are consistent with what we prefer to believe, and all too ready to ignore any facts that don’t. Climate change deniers, for instance, sometimes insist that the global temperature did not go up during the seventeen years between 1998 and 2015, only because they cherry-picked 1998 as their base year (which had an artificially high temperature due to El Niño).6 The problem here is one of bad faith.

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This is achieved by cherry-picking lists of dissenters, who may or may not have any expertise in this area. The Greenpeace report Twenty Years of Failure states that it is a “myth” to think that GMO foods are safe to eat and claims that “there is no scientific consensus on the safety of GM foods.” But, as Lynas argues: [This] requires extreme selection bias. This is the ultimate in cherry picking. Greenpeace highlights a statement by a small group of dissenters, while ignoring the National Academy of Sciences, the American Association for the Advancement of Science, the Royal Society, the African Academy of Sciences, the European Academies of Science Advisory Council, the French Academy of Science, the American Medical Association, the Union of German Academies of Science and Humanities and numerous others.85 Belief in Conspiracy Theories As we see in Stephan Lewandowsky’s work, adherence to conspiracy theories is an essential part of science denial.

Black Box Thinking: Why Most People Never Learn From Their Mistakes--But Some Do
by Matthew Syed
Published 3 Nov 2015

It is possible that only the parents of children whose behavior improved bothered to respond. Parents whose kids continued to behave badly might have thrown the questionnaire in the bin, or at least responded in fewer numbers. This could have skewed the stats beyond recognition. This is a type of so-called “selection bias” and it should sound familiar. It is pretty much the same problem that bedeviled medieval medicine when only those who recovered from bloodletting were able to testify to its effectiveness. The evidence sounded terrific but that is because it was dangerously incomplete. Those who did not recover from bloodletting were never given a chance to express an opinion.

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20 Years Later (documentary), 159 Scared Straight program, 150–54, 159–67 Campbell Corporation’s systematic review of, 164–65 Finnckenauer’s randomized control trial (RCT) of, 160, 162–64 Scheck, Barry, 67, 68, 70, 77, 78, 80, 82, 84, 85, 117 Schulz, Kathryn, 78–79, 81 Schumpeter, Joseph, 130 science, 41–45, 48 ancient Greeks and, 278–79 Bacon’s criticism of medieval, 279–80, 283 failure and, 266 history of, 277–82 Lysenko and, 108–10 method and mindset of, 51–52 scurvy, 56 second victim, 239 selection bias, 161–62 self-esteem, 74, 75–76, 82, 90, 97, 98, 101, 274 self-handicapping, 272–74 self-justification, 18, 87, 88–89, 90, 97–99 and Iraq War decisions, 92–93 Shapiro, Arnold, 153, 166 Shepherd-Barron, John, 196 Shirley, Michael, 69 Shoemaker, Paul, 102 Shoesmith, Sharon, 236, 239 signatures, 11, 18, 24, 52 Simeone, Diego, 274 Simons, Daniel, 117 Sims, Peter, 139–40, 144 Singer, Paul, 95 Skiles, Jeffrey, 38, 39 Slemmer, Mike, 138–40 soccer, 135–36, 253–55, 274–76, 289–90 social hierarchies, as inhibiting assertiveness, 28–29 Social Science and Medical Journal, 89 social tolerance, 285 social workers, 236–38, 239 social world, 283–87 Socrates, 278 software design, 138–40 South Korean ferry disaster, 12 Soyfer, Valery, 109 speed-eating, 187–88 Spelling Bees, 263 Speziale, Angelo, 165–67 sports, 132n, 135–36, 266, 289–90.

Battling Eight Giants: Basic Income Now
by Guy Standing
Published 19 Mar 2020

Torry, ‘An Update, a Correction, and an Extension, of an Evaluation of an Illustrative Citizen’s Basic Income Scheme’. 5 There are two other options: (1) Different amounts paid in several areas (e.g. £50 in one area, £75 in another) to see if that affects behaviour and attitudes. In principle, this writer is opposed to this. (2) Proceed by inviting locals to participate, and then select from those ‘volunteering’. This is also dubious, partly due to selectivity bias. 6 A. B. Atkinson, Inequality: What Can Be Done?, London and Cambridge, MA: Harvard University Press, 2015, chapter 8. 7 Torry, The Feasibility of Citizen’s Income, pp. 134–9. 8 For a fuller critique, see ‘Symposium: Anthony Atkinson’s “The Case for a Participation Income”’, Political Quarterly 89(2), April–June 2018; Standing, Basic Income, pp. 175–6; M.

Practical Manual of Thyroid and Parathyroid Disease
by Asit Arora , Neil Tolley and R. Michael Tuttle
Published 2 Jan 2009

There does not appear to be any difference between a multinodular goitre and a solitary nodule regarding the prevalence of malignancy.2–5 Recent retrospective studies of incidental thyroid nodules detected on ultrasound have demonstrated a rate of malignancy approaching 30%.6,7 Although these latter studies were performed in tertiary referral centres and are therefore prone to selection bias, it A Practical Manual of Thyroid and Parathyroid Disease, 1st Edition. Edited by Asit Arora, Neil Tolley & R. Michael Tuttle. © 2010 Blackwell Publishing 36 appears that incidental nodules detected on [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) have an even higher rate of malignancy, exceeding 40%.8 Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy.

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In view of these findings, we advocate close follow-up, with surgery reserved for clinically recurrent disease.88 The role of post-operative radiotherapy is controversial due to lack of prospective studies. Retrospective series comparing surgery alone with surgery and radiotherapy are subject to selection bias. A favourable response in terms of tumour reduction and local control has been reported.89,90 At the Institut Gustave-Roussy, the survival of 68 patients treated with surgery alone was similar to that of 59 patients who received post-operative radiotherapy. However, in patients with involved lymph nodes, 5-year survival improved significantly with postoperative radiotherapy from 36 to 81%.91 In contrast, an adverse effect of radiotherapy was reported from the M.D.

The Science of Hate: How Prejudice Becomes Hate and What We Can Do to Stop It
by Matthew Williams
Published 23 Mar 2021

In step 2, the data from areas where high correlations are found (e.g. the amygdala and insula) are averaged to produce the published results. This approach distorts the picture by only selecting highly correlated results from the ‘noise’, thus presenting the pattern that is being searched for. This is akin to the selection bias problem (see E. Vul et al., ‘Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition’, Perspectives on Psychological Science 4 (2009), 274–90). ‡‡ This correction is used when multiple tests for statistical significance are performed, as they are in brain imaging studies.

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Abdallah, Abdalraouf, 1 Abedi, Salman, 1, 2, 3, 4 abortion, 1, 2 Abu Sayyaf Group, 1 abuse, 1, 2, 3, 4, 5 accelerants to hate, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 accelerationists, 1 addiction, 1, 2, 3, 4 Admiral Duncan bar, 1 adolescence, 1, 2, 3, 4, 5, 6, 7 advertising, 1, 2, 3, 4, 5 African Americans, 1, 2, 3, 4, 5, 6, 7 afterlife, 1, 2 age, 1, 2 aggression: brain and hate, 1, 2, 3, 4, 5; false alarms, 1; group threat, 1, 2, 3, 4, 5, 6; identity fusion, 1; mortality, 1; pyramid of hate, 1; trauma and containment, 1, 2 AI, see artificial intelligence Albright, Jonathan, 1 alcohol, 1, 2, 3, 4, 5, 6, 7, 8 algorithms: far-right hate, 1, 2, 3, 4; filter bubbles and bias, 1, 2; Google, 1, 2, 3; online hate speech, 1, 2, 3, 4, 5, 6; Tay, 1, 2; tipping point, 1, 2; YouTube, 1 Algotransparency.org, 1 Allport, Gordon, 1, 2, 3, 4 Al Noor Mosque, Christchurch, 1 al-Qaeda, 1, 2 Alternative für Deutschland (AfD), 1 alt-right: algorithms, 1, 2; brain and hate, 1; Charlottesville rally, 1, 2; counter-hate speech, 1; definition, 1n; Discord, 1; Facebook, 1, 2, 3; fake accounts, 1; filter bubbles, 1, 2; red-pilling, 1, 2; social media, 1, 2; Trump, 1, 2; YouTube, 1 Alzheimer’s disease, 1 American Crowbar Case, 1 American culture, 1 American Nazi Party, 1, 2 Amodio, David, 1n amygdala: brain and signs of prejudice, 1, 2; brain tumours, 1; disengaging the amygdala autopilot, 1; hate and feeling pain, 1, 2; and insula, 1; neuroscience of hate, 1n, 2, 3, 4; parts that edge us towards hate, 1; parts that process prejudice, 1; prepared versus learned amygdala responses, 1, 2; processing of ‘gut-deep’ hate, 1; recognising facial expressions, 1n, 2; stopping hate, 1, 2; trauma and containment, 1, 2; unlearning prejudiced threat detection, 1 anger, 1, 2, 3, 4, 5, 6, 7, 8 anonymity, 1, 2 anterior insula, 1n Antifa, 1, 2n, 3 anti-gay prejudice, 1, 2, 3, 4, 5, 6, 7, 8 anti-hate initiatives, 1, 2 antilocution, 1 anti-Muslim hate, 1, 2, 3, 4, 5, 6 anti-Semitism, 1, 2, 3, 4, 5, 6 anti-white hate crime, 1 Antonissen, Kirsten, 1, 2 anxiety: brain and hate, 1, 2, 3, 4; harm of hate speech, 1; intergroup contact, 1, 2; subcultures of hate, 1, 2; trauma and containment, 1; trigger events, 1, 2 Arab people, 1, 2, 3, 4, 5, 6 Arbery, Ahmaud, 1 Arkansas, 1, 2 artificial intelligence (AI), 1, 2, 3, 4 Asian Americans, 1, 2 Asian people, 1, 2, 3, 4 assault, 1, 2, 3 asylum seekers, 1, 2, 3, 4 Athens, 1 Atlanta attack, 1 Atran, Scott, 1, 2 attachment, 1 attention, 1, 2, 3 attitudes, 1, 2, 3, 4, 5, 6 Aung San Suu Kyi, 1 austerity, 1 Australia, 1 autism, 1 averages, 1, 2 avoidance, 1, 2, 3 Bali attack, 1 Bangladeshi people, 1 BBC (British Broadcasting Corporation), 1, 2, 3 behavioural sciences, 1, 2 behaviour change, 1, 2, 3 beliefs, 1, 2, 3 Bell, Sean, 1, 2 Berger, Luciana, 1 Berlin attacks, 1 bias: algorithms, 1; brain and hate, 1, 2, 3, 4, 5, 6, 7; filter bubbles, 1; Google Translate, 1; group threat, 1, 2, 3, 4; police racial bias, 1; predicting hate crime, 1; stopping hate, 1, 2, 3; unconscious bias, 1, 2, 3, 4 Bible, 1 Biden, Joe, 1 ‘Big Five’ personality traits, 1 biology, 1, 2, 3, 4, 5, 6, 7 Birstall, 1 bisexual people, 1 Black, Derek, 1, 2 Black, Don, 1, 2, 3 blackface, 1 Black Lives Matter, 1 Black Mirror, 1n black people: author’s brain and hate, 1, 2, 3, 4, 5; brain and signs of prejudice, 1, 2; brain parts that edge us towards hate, 1; brain parts that process prejudice, 1; Charlottesville rally, 1, 2; disengaging the amygdala autopilot, 1; Duggan shooting, 1; feeling pain, 1; Google searches, 1, 2; group threat, 1, 2, 3, 4; online hate speech, 1, 2, 3, 4; police relations, 1, 2; predicting hate crime, 1, 2; prepared versus learned amygdala responses, 1; pyramid of hate, 1, 2, 3n; recognising facial expressions, 1, 2; South Africa, 1; steps to stop hate, 1, 2, 3, 4; trauma and Franklin, 1, 2, 3, 4; trigger events, 1, 2, 3; unconscious bias, 1; unlearning prejudiced threat detection, 1, 2; white flight, 1 BNP, see British National Party Bolsonaro, Jair, 1 Bosnia and Herzegovina, 1, 2 bots, 1, 2, 3, 4, 5 Bowers, Robert Gregory, 1 boys, 1, 2 Bradford, 1 brain: ancient brains in modern world, 1; author’s brain and hate, 1; beyond the brain, 1; the brain and hate, 1; brain and signs of prejudice, 1; brain damage and tumours, 1, 2, 3, 4; brains and unconscious bias against ‘them’, 1; brain’s processing of ‘gut-deep’ hate, 1; defence mechanisms, 1; disengaging the amygdala autopilot, 1; figures, 1; finding a neuroscientist and brain scanner, 1; group threat detection, 1, 2; hacking the brain to hate, 1; hate and feeling pain, 1; locating hate in the brain, 1; neuroscience and big questions about hate, 1; overview, 1; parts that edge us towards hate, 1; parts that process prejudice, 1; prepared versus learned amygdala responses, 1; recognising facial expressions, 1; rest of the brain, 1; signs of prejudice, 1; steps to stop hate, 1, 2; tipping point to hate, 1, 2, 3, 4, 5; trauma and containment, 1, 2; unlearning prejudiced threat detection, 1; where neuroscience of hate falls down, 1 brain imaging: author’s brain and hate, 1; beyond the brain, 1; the brain and hate, 1; brain and signs of prejudice, 1, 2; brain injury, 1, 2; Diffusion MRI, 1; disengaging the amygdala autopilot, 1; finding a neuroscientist and brain scanner, 1; fusiform face area, 1; locating hate in the brain, 1; MEG, 1; neuroscience of hate, 1, 2, 3; parts that process prejudice, 1; prepared versus learned amygdala responses, 1; processing of ‘gut-deep’ hate, 1; subcultures of hate, 1, 2; unconscious bias, 1 brainwashing, 1, 2 Bray, Mark, 1n Brazil, 1, 2, 3 Breivik, Anders, 1, 2 Brexit, 1, 2, 3, 4n, 5, 6, 7, 8, 9 Brexit Party, 1, 2 Brick Lane, London, 1 Britain First, 1, 2 British identity, 1, 2 British National Party (BNP), 1, 2n, 3, 4, 5 Brixton, 1 Broadmoor Hospital, 1, 2 Brooker, Charlie, 1n Brooks, Rayshard, 1 Brown, Katie, 1, 2 Brown, Michael, 1, 2 Brussels attack, 1 Budapest Pride, 1 bullying, 1, 2 Bundy, Ted, 1 burka, 1, 2, 3 Burmese, 1 Bush, George W., 1 Byrd, James, Jr, 1 California, 1, 2n, 3 Caliskan, Aylin, 1 Cambridge Analytica, 1, 2 cancer, 1, 2 Cardiff University Brain Research Imaging Centre (CUBRIC), 1, 2, 3, 4 caregiving motivational system, 1 care homes, 1, 2 Casablanca, 1 cascade effect, 1, 2 categorisation, 1, 2, 3, 4 Catholics, 1 Caucasian Crew, 1 causality, 1, 2 celebrities, 1, 2, 3, 4 censorship, 1, 2 Centennial Olympic Park, Atlanta, 1 Centers for Disease Control (CDC), 1 change blindness, 1 charity, 1, 2, 3 Charlottesville rally, 1, 2, 3n, 4 chatbots, 1, 2, 3 Chauvin, Derek, 1 Chelmsford, 1 Chicago, 1 childhood: attachment issues, 1; child abuse, 1, 2, 3; child grooming, 1; child play, 1; failures of containment, 1, 2, 3, 4; group threat, 1, 2; intergroup contact, 1, 2; learned stereotypes, 1; online hate speech, 1, 2; predicting hate crime, 1; trauma and containment, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10; trigger events, 1, 2; understanding the ‘average’ hate criminal, 1; understanding the ‘exceptional’ hate offender, 1, 2, 3 China, 1, 2, 3, 4 Chinese people, 1, 2, 3 ‘Chinese virus,’ 1, 2 Cho, John, 1 Christchurch mosque attack, 1 Christianity, 1, 2, 3 cinema, 1 citizen journalism, 1 civilising process, 1 civil rights, 1, 2, 3, 4 class, 1, 2 cleaning, 1 climate change, 1, 2 Clinton, Hillary, 1, 2 cognitive behavioural therapy, 1 cognitive dissonance, 1 Cohen, Florette, 1, 2 Cold War, 1 collective humiliation, 1 collective quests for significance, 1, 2 collective trauma, 1, 2 colonialism, 1n, 2 Combat 1, 2 comedies, 1, 2, 3 Communications Acts, 1, 2 compassion, 1, 2, 3 competition, 1, 2, 3, 4, 5, 6, 7, 8 confirmation bias, 1 conflict, 1, 2, 3, 4 conflict resolution, 1, 2, 3, 4, 5 Connectome, 1 Conroy, Jeffrey, 1 Conservative Party, 1, 2, 3 conspiracy theories, 1, 2, 3 contact with others, 1, 2 containment: failures of, 1; hate as container of unresolved trauma, 1; understanding the ‘exceptional’ hate offender, 1, 2, 3 content moderation, 1, 2, 3 context, 1, 2, 3 Convention of Cybercrime, 1 cooperation, 1, 2, 3, 4, 5, 6 Copeland, David, 1, 2, 3, 4, 5, 6, 7 coping mechanisms, 1, 2, 3, 4, 5, 6, 7 Cordoba House (‘Ground Zero mosque’), 1 correction for multiple comparisons, 1, 2n ‘corrective rape’, 1, 2 cortisol, 1 Council of Conservative Citizens, 1n counter-hate speech, 1, 2, 3, 4 courts, 1, 2, 3, 4, 5, 6 COVID-19 pandemic, 1, 2, 3 Cox, Jo, 1, 2, 3 Criado Perez, Caroline, 1 crime, 1, 2, 3, 4, 5, 6, 7 Crime and Disorder Act 1998, 1n crime recording, 1, 2, 3, 4 crime reporting, 1, 2, 3, 4, 5, 6, 7 Crime Survey for England and Wales (CSEW), 1 criminal justice, 1, 2, 3 Criminal Justice Act, 1, 2n criminal prosecution, 1, 2 criminology, 1, 2, 3, 4, 5, 6 cross-categorisation, 1 cross-race or same-race effect, 1 Crusius, Patrick, 1, 2 CUBRIC (Cardiff University Brain Research Imaging Centre), 1, 2, 3, 4 cultural ‘feeding’, 1, 2, 3, 4, 5 cultural worldviews, 1, 2, 3, 4, 5, 6, 7 culture: definitions, 1; group threat, 1, 2, 3; steps to stop hate, 1, 2, 3; tipping point, 1, 2, 3, 4, 5; unlearning prejudiced threat detection, 1 culture machine, 1, 2, 3, 4, 5 culture wars, 1 Curry and Chips, 1 cybercrime, 1 dACC, see dorsal anterior cingulate cortex Daily Mail, 1, 2 Dailymotion, 1 Daily Stormer, 1, 2n Daley, Tom, 1, 2 Darfur, 1 dark matter, 1 death: events that remind us of our mortality, 1; newspapers, 1; predicting hate crime, 1; religion and hate, 1, 2; subcultures of hate, 1, 2; trigger events, 1, 2 death penalty, 1, 2 death threats, 1 decategorisation, 1 De Dreu, Carsten, 1, 2, 3, 4 deep learning, 1, 2 defence mechanisms, 1 defensive haters, 1, 2 dehumanisation, 1, 2, 3, 4, 5, 6 deindividuation, 1, 2 deindustrialisation, 1, 2, 3, 4 Democrats, 1, 2, 3 Denny, Reginald, 1 DeSalvo, Albert (the Boston Strangler), 1 desegregation, 1, 2, 3 Desmond, Matthew, 1 Dewsbury, 1, 2, 3 Diffusion Magnetic Resonance Imaging (Diffusion MRI), 1, 2 diminished responsibility, 1, 2 Director of Public Prosecutions (DPP), 1 disability: brain and hate, 1, 2; group threat, 1, 2, 3, 4, 5, 6; intergroup contact, 1; Japan care home, 1, 2; online hate speech, 1; profiling the hater, 1; suppressing prejudice, 1; victim perception, 1n Discord, 1, 2, 3, 4 discrimination: brain and hate, 1, 2; comedy programmes, 1; Google searches, 1; Japan laws, 1; preference for ingroup, 1; pyramid of hate, 1, 2, 3; questioning prejudgements, 1; trigger events, 1, 2, 3 disgust: brain and hate, 1, 2, 3, 4, 5, 6; group threat detection, 1, 2, 3; ‘gut-deep’ hate, 1, 2; Japan care home, 1; what it means to hate, 1, 2 disinformation, 1, 2, 3 displacement, 1, 2 diversity, 1, 2, 3 dlPFC, see dorsolateral prefrontal cortex domestic violence, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 Doran, John, 1, 2, 3 dorsal anterior cingulate cortex (dACC), 1, 2, 3n, 4, 5, 6, 7, 8, 9 dorsolateral prefrontal cortex (dlPFC), 1n, 2, 3 Douglas, Mary, Purity and Danger, 1 drag queens, 1 drugs, 1, 2, 3, 4, 5, 6, 7, 8, 9 Duggan, Mark, 1 Duke, David, 1 Dumit, Joe, Picturing Personhood, 1 Durkheim, Emile, 1 Dykes, Andrea, 1 Earnest, John T., 1 Eastern Europeans, 1, 2, 3 Ebrahimi, Bijan, 1, 2, 3, 4, 5, 6 echo chambers, 1, 2n economy, 1, 2, 3, 4, 5, 6 EDL, see English Defence League education, 1, 2, 3, 4 Edwards, G., 1 8chan, 1, 2 elections, 1, 2, 3, 4, 5, 6 electroencephalography, 1n elites, 1 ELIZA (computer program), 1 The Ellen Show, 1 El Paso shooting, 1 Elrod, Terry, 1 Emancipation Park, Charlottesville, 1 Emanuel African Methodist Church, Charleston, 1 emotions: brain and hate, 1, 2, 3, 4n, 5, 6, 7, 8, 9; group threat, 1; subcultures of hate, 1; trigger events and mortality, 1; what it means to hate, 1, 2, 3, 4 empathy: brain and hate, 1, 2, 3, 4, 5, 6; feeling hate together, 1; group threat, 1, 2; steps to stop hate, 1, 2, 3; subcultures of hate, 1; trauma and containment, 1 employment, 1, 2, 3, 4, 5, 6, 7 English Defence League (EDL), 1, 2n, 3 epilepsy, 1, 2, 3, 4, 5 Epstein, Robert, 1 equality, 1, 2 Essex, 1 ethnicity, 1, 2n, 3, 4 ethnic minorities, 1, 2, 3, 4, 5, 6 ethnocentrism, 1 EU, see European Union European Commission, 1, 2 European Digital Services Act, 1 European Parliament, 1, 2 European Social Survey, 1 European Union (EU): Brexit referendum, 1, 2, 3, 4n, 5; Facebook misinformation, 1; group threat, 1, 2; online hate speech, 1, 2, 3; trigger events, 1 Eurovision, 1 evidence-based hate crime, 1 evolution, 1, 2, 3, 4, 5, 6, 7, 8 executive control area: brain and hate, 1, 2, 3, 4, 5, 6, 7, 8; disengaging the amygdala autopilot, 1, 2; extremism, 1; recognising false alarms, 1; trauma and containment, 1; trigger events, 1 exogenous shocks, 1 expert opinion, 1 extreme right, 1, 2, 3, 4, 5 extremism: Charlottesville and redpilling, 1, 2; feeling hate together, 1; online hate speech, 1; perceiving versus proving hate, 1; quest for significance, 1, 2, 3; subcultures of hate, 1, 2, 3, 4, 5, 6, 7; trauma and containment, 1; trigger events, 1, 2, 3 Facebook: algorithms, 1, 2; Charlottesville rally, 1, 2; Christchurch mosque attack, 1; far-right hate, 1, 2, 3, 4, 5; filter bubbles, 1, 2; how much online hate speech, 1, 2; Myanmar genocide, 1; online hate and offline harm, 1, 2, 3; redpilling, 1; stopping online hate speech, 1, 2, 3, 4 facial expression, 1, 2, 3, 4 faith, 1, 2 fake accounts, 1, 2; see also bots fake news, 1, 2, 3, 4 false alarms, 1, 2, 3 Farage, Nigel, 1, 2 far left, 1n, 2, 3, 4 Farook, Syed Rizwan, 1 far right: algorithms, 1, 2, 3, 4; brain injury, 1; Charlottesville rally, 1, 2, 3n, 4; COVID-19 pandemic, 1, 2; Facebook, 1, 2, 3, 4, 5; filter bubbles, 1, 2; gateway sites, 1; group threat, 1, 2; red-pilling, 1; rise of, 1; stopping online hate speech, 1; subcultures of hate, 1, 2, 3, 4, 5; terror attacks, 1, 2, 3; tipping point, 1, 2; trauma and containment, 1, 2, 3, 4n; trigger events, 1, 2; YouTube, 1 fathers, 1, 2, 3 FBI, see Federal Bureau of Investigation fear: brain and hate, 1, 2, 3, 4, 5, 6, 7; feeling hate together, 1; group threat, 1, 2, 3, 4, 5; mortality, 1; online hate speech, 1, 2, 3; steps to stop hate, 1, 2; trauma and containment, 1, 2; trigger events, 1, 2, 3 Federal Bureau of Investigation (FBI), 1, 2, 3, 4, 5, 6, 7 Federation of American Immigration Reform, 1 Ferguson, Missouri, 1 Festinger, Leon, 1 fiction, 1 Fields, Ted, 1 50 Cent Army, 1 ‘fight or flight’ response, 1, 2, 3 films, 1, 2 filter bubbles, 1, 2, 3, 4 Finland, 1, 2, 3, 4, 5, 6 Finsbury Park mosque attack, 1, 2, 3 first responders, 1 Fiske, Susan, 1 Five Star Movement, 1 flashbacks, 1 Florida, 1, 2 Floyd, George, 1, 2, 3 Flynt, Larry, 1 fMRI (functional Magnetic Resonance Imaging), 1, 2, 3, 4, 5, 6, 7 football, 1, 2, 3, 4, 5 football hooligans, 1, 2 Forever Welcome, 1 4chan, 1, 2 Fox News, 1, 2 Franklin, Benjamin, 1 Franklin, Joseph Paul, 1, 2, 3, 4, 5, 6, 7, 8 Fransen, Jayda, 1 freedom fighters, 1, 2 freedom of speech, 1, 2, 3, 4, 5, 6 frustration, 1, 2, 3, 4 functional Magnetic Resonance Imaging (fMRI), 1, 2, 3, 4, 5, 6, 7 fundamentalism, 1, 2 fusiform face area, 1 fusion, see identity fusion Gab, 1 Gadd, David, 1, 2n, 3, 4 Gaddafi, Muammar, 1, 2 Gage, Phineas, 1, 2 galvanic skin responses, 1 Gamergate, 1 gateway sites, 1 gay people: author’s experience, 1, 2, 3; brain and hate, 1, 2; Copeland attacks, 1, 2; COVID-19 pandemic, 1; filter bubbles, 1; gay laws, 1; gay marriage, 1, 2, 3; group associations, 1; group threat, 1, 2, 3, 4, 5; hate counts, 1, 2, 3, 4; physical attacks, 1, 2; profiling the hater, 1; Russia, 1, 2, 3, 4, 5; Section 1, 2, 3, 4; steps to stop hate, 1, 2, 3; trigger events, 1, 2; why online hate speech hurts, 1; see also LGBTQ+ people gay rights, 1, 2, 3, 4 gender, 1, 2, 3, 4, 5, 6, 7 Generation Identity, 1 Generation Z, 1, 2 genetics, 1n, 2, 3 genocide, 1, 2, 3, 4, 5, 6 Georgia (country), 1 Georgia, US, 1, 2, 3, 4 Germany, 1, 2, 3, 4, 5, 6, 7 Gilead, Michael, 1 ginger people, 1 girls, and online hate speech, 1 Gladwell, Malcolm, 1 Global Project Against Hate and Extremism, 1 glucocorticoids, 1, 2 God, 1, 2 God’s Will, 1, 2 Goebbels, Joseph, 1 Google, 1, 2, 3, 4, 5, 6, 7, 8 Google+, 1 Google Translate, 1 goth identity, 1, 2, 3, 4 governments, 1, 2, 3, 4, 5, 6 Grant, Oscar, 1 gravitational waves, 1 Great Recession (2007–9), 1 Great Replacement conspiracy theory, 1 Greece, 1, 2 Greenberg, Jeff, 1, 2, 3 Greene, Robert, 1 grey matter, 1 Grillot, Ian, 1, 2 Grodzins, Morton, 1 grooming, 1, 2, 3 ‘Ground Zero mosque’ (Cordoba House), 1 GroupMe, 1 groups: ancient brains in modern world, 1; brain and hate, 1, 2, 3, 4; childhood, 1; feeling hate together, 1; foundations of prejudice, 1; group threat and hate, 1; identity fusion, 1, 2, 3; intergroup hate, 1; pyramid of hate, 1; reasons for hate offending, 1; steps to stop hate, 1, 2; tipping point, 1, 2, 3, 4; warrior psychology, 1, 2, 3; what it means to hate, 1, 2 group threat, 1; beyond threat, 1; Bijan as the threatening racial other, 1; context and threat, 1; cultural machine, group threat and stereotypes, 1; evolution of group threat detection, 1; human biology and threat, 1; neutralising the perception of threat, 1; overview, 1; society, competition and threat, 1; threat in their own words, 1 guilt, 1, 2, 3, 4 guns, 1, 2 ‘gut-deep’ hate, 1, 2, 3, 4 Haines, Matt, 1 Haka, 1 Halle Berry neuron, 1, 2 harassment, 1, 2, 3, 4, 5 harm of hate, 1, 2, 3, 4, 5, 6, 7 Harris, Brendan, 1 Harris, Lasana, 1 Harris, Lovell, 1, 2, 3, 4 hate: author’s brain and hate, 1; the brain and hate, 1; definitions, 1, 2; feeling hate together, 1; foundations of prejudice and hate, 1, 2, 3; group threat and hate, 1; ‘gut-deep’ hate, 1, 2; hate counts, 1; hate in word and deed, 1; profiling the hater, 1; pyramid of hate, 1; rise of the bots and trolls, 1; seven steps to stop hate, 1; subcultures of hate, 1; tipping point from prejudice to hate, 1; trauma, containment and hate, 1; trigger events and ebb and flow of hate, 1; what it means to hate, 1 hate counts, 1; criminalising hate, 1; how they count, 1; overview, 1; perceiving versus proving hate, 1; police and hate, 1; rising hate count, 1; ‘signal’ hate acts and criminalisation, 1; Sophie Lancaster, 1; warped world of hate, 1 hate crime: author’s experience, 1, 2, 3; brain and hate, 1, 2, 3, 4, 5; definitions, 1; events and hate online, 1; events and hate on the streets, 1, 2; the ‘exceptional’ hate criminal, 1; far-right hate, 1, 2, 3; foundations of prejudice and hate, 1, 2, 3, 4; group threat, 1, 2, 3, 4, 5, 6, 7, 8; hate counts, 1, 2, 3, 4, 5; laws, 1n, 2, 3, 4, 5; number of crimes, 1, 2; online hate speech, 1, 2, 3, 4; predicting hate crime, 1; profiling the hater, 1; steps to stop hate, 1, 2, 3; trauma and containment, 1, 2, 3, 4; trigger events, 1, 2, 3, 4, 5, 6; understanding the ‘average’ hate criminal, 1; understanding the ‘exceptional’ hate offender, 1; what it means to hate, 1, 2, 3 hate groups, 1, 2, 3, 4, 5 hate in word and deed, 1; algorithmic far right, 1; Charlottesville rally, 1, 2, 3n, 4; extreme filter bubbles, 1; game changer for the far right, 1; gateway sites, 1; overview, 1; ‘real life effort post’ and Christchurch, 1; red-pilling, 1 HateLab, 1, 2, 3, 4, 5 hate speech: far-right hate, 1, 2, 3; filter bubbles and bias, 1; harm of, 1; how much online hate speech, 1; Japan laws, 1; pyramid of hate, 1; stopping online hate speech, 1; Tay chatbot, 1; trigger events, 1, 2, 3; why online hate speech hurts, 1 hate studies, 1, 2 ‘hazing’ practices, 1 health, 1, 2, 3, 4 Henderson, Russell, 1 Herbert, Ryan, 1 Hewstone, Miles, 1 Heyer, Heather, 1 Hinduism, 1, 2 hippocampus, 1, 2, 3, 4 history of offender, 1 Hitler, Adolf, 1, 2, 3, 4, 5, 6, 7 HIV/AIDS, 1, 2, 3, 4, 5, 6, 7 hollow mask illusion, 1, 2 Hollywood, 1, 2 Holocaust, 1, 2, 3, 4 Homicide Act, 1n homophobia: author’s experience, 1, 2, 3, 4; brain and hate, 1, 2, 3; evidence-based hate crime, 1; federal law, 1; jokes, 1; online hate speech, 1, 2; Russia, 1, 2; Shepard murder, 1; South Africa, 1; trauma and containment, 1; victim perception of motivation, 1n Homo sapiens, 1 homosexuality: author’s experience, 1; online hate speech, 1; policing, 1; questioning prejudgements, 1; Russia, 1, 2; trauma and containment, 1, 2; see also gay people hooligans, 1, 2 Horace, 1 hormones, 1, 2, 3 hot emotions, 1 hot-sauce study, 1, 2 housing, 1, 2, 3, 4, 5, 6 Huddersfield child grooming, 1 human rights, 1, 2, 3 humiliation, 1, 2, 3, 4, 5, 6 humour, 1, 2 Hungary, 1 hunter-gatherers, 1n, 2 Hustler, 1 IAT, see Implicit Association Test identity: author’s experience of attack, 1; British identity, 1, 2; Charlottesville rally, 1, 2; children’s ingroups, 1; group threat, 1, 2; online hate speech, 1, 2, 3, 4; steps to stop hate, 1, 2 identity fusion: fusion and hateful murder, 1; fusion and hateful violence, 1; fusion and self-sacrifice in the name of hate, 1; generosity towards the group, 1; tipping point, 1, 2; warrior psychology, 1, 2, 3 ideology, 1, 2, 3, 4 illegal hate speech, 1, 2, 3, 4 illocutionary speech, 1 imaging, see brain imaging immigration: Forever Welcome, 1; group threat, 1, 2, 3, 4, 5, 6, 7; hate counts, 1n, 2; HateLab Brexit study, 1; identity fusion, 1; intergroup contact, 1; negative stereotypes, 1; online hate speech, 1; Purinton, 1, 2; trauma and containment, 1, 2, 3; trigger events, 1, 2n, 3, 4, 5, 6, 7; YouTube algorithms, 1 immortality, 1, 2 Implicit Association Test (IAT), 1, 2, 3, 4, 5, 6, 7, 8, 9 implicit prejudice: author’s brain and hate, 1, 2, 3, 4; brain and hate, 1, 2, 3, 4, 5, 6; online hate speech, 1, 2 India, 1 Indonesia, 1 Infowars, 1, 2 Ingersoll, Karma, 1 ingroup: brain and hate, 1, 2, 3, 4; child play, 1; group threat, 1, 2, 3, 4, 5, 6, 7; HateLab Brexit study, 1; identity fusion, 1, 2; pyramid of hate, 1; reasons for hate offending, 1; trigger events, 1, 2, 3; what it means to hate, 1, 2, 3, 4, 5 Instagram, 1, 2, 3 Institute for Strategic Dialogue, 1 institutional racism, 1 instrumental crimes, 1 insula: brain and signs of prejudice, 1, 2, 3; facial expressions, 1, 2; fusiform face area, 1; hacking the brain to hate, 1; hate and feeling pain, 1; neuroscience of hate, 1n, 2, 3, 4, 5; parts that edge us towards hate, 1; parts that process prejudice, 1; processing of ‘gut-deep’ hate, 1, 2 Integrated Threat Theory (ITT), 1, 2, 3 integration, 1, 2, 3, 4 intergroup contact, 1, 2, 3 Intergroup Contact Theory, 1, 2, 3 intergroup hate, 1, 2, 3, 4 internet: algorithms, 1, 2; chatbots, 1; counterhate speech, 1; COVID-19 pandemic, 1; far-right hate, 1, 2, 3, 4, 5, 6, 7; filter bubbles, 1, 2, 3; Google searches, 1; hate speech harm, 1; how much online hate speech, 1; online news, 1; reasons for hate offending, 1; rise of the bots and trolls, 1; stopping online hate speech, 1; tipping point, 1, 2, 3; training the machine to count hate, 1; why online hate speech hurts, 1 interracial relations, 1, 2, 3, 4 intolerance, 1, 2 Iranian bots, 1 Iraq, 1 Irish Republican Army (IRA), 1 ISIS, 1, 2, 3, 4, 5, 6, 7, 8, 9 Islam: group threat, 1; online hate speech, 1, 2, 3, 4, 5; steps to stop hate, 1, 2, 3; subcultures of hate, 1, 2, 3, 4; trigger events, 1, 2, 3 Islamism: group threat, 1; online hate speech, 1, 2, 3, 4; profiling the hater, 1; subcultures of hate, 1, 2, 3; trigger events, 1, 2, 3 Islamophobia, 1, 2, 3, 4 Israel, 1, 2, 3 Italy, 1, 2 ITT, see Integrated Threat Theory James, Lee, 1, 2, 3, 4, 5, 6 Japan, 1, 2, 3 Jasko, Katarzyna, 1 Jefferson, Thomas, 1 Jenny Lives with Eric and Martin, 1 Jewish people: COVID-19 pandemic, 1, 2; far-right hate, 1, 2, 3, 4, 5; filter bubbles, 1; Google searches, 1, 2; group threat, 1; Nazism, 1, 2; negative stereotypes, 1 2 online hate speech, 1; pyramid of hate, 1; questioning prejudgements, 1; ritual washing, 1; subcultures of hate, 1, 2; trauma and Franklin, 1, 2, 3 jihad, 1, 2, 3, 4, 5 jokes, 1, 2, 3, 4, 5, 6, 7 Jones, Alex, 1 Jones, Terry, 1 Josephson junction, 1 Judaism, 1; see also Jewish people Jude, Frank, Jr, 1, 2, 3, 4, 5 Kansas, 1 Kerry, John, 1 Kik, 1 King, Gary, 1 King, Martin Luther, Jr, 1, 2 King, Rodney, 1, 2, 3 King, Ryan, 1 Kirklees, 1, 2 KKK, see Ku Klux Klan Kuchibhotla, Srinivas, 1, 2, 3, 4 Kuchibhotla, Sunayana, 1, 2 Ku Klux Klan (KKK), 1, 2, 3n, 4, 5, 6, 7 Labour Party, 1, 2, 3 Lancaster, Sophie, 1, 2 language, 1, 2, 3, 4, 5, 6, 7 LAPD (Los Angeles Police Department), 1 Lapshyn, Pavlo, 1 Lashkar-e-Taiba, 1 Las Vegas shooting, 1, 2 Latinx people, 1, 2, 3, 4, 5, 6, 7 law: brain and hate, 1, 2, 3; criminalising hate, 1; hate counts, 1, 2, 3; Kansas shooting, 1; limited laws, 1; online hate speech, 1; pyramid of hate, 1 Law Commission, 1 Lawrence, Stephen, 1 learned fears, 1, 2, 3 Leave.EU campaign, 1, 2 Leave voters, 1, 2, 3n Lee, Robert E., 1, 2, 3 left orbitofrontal cortex, 1n, 2n Legewie, Joscha, 1, 2, 3, 4 lesbians, 1, 2 Levin, Jack, 1 LGBTQ+ people, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17; see also gay people LIB, see Linguistic Intergroup Bias test Liberman, Nira, 1 Liberty Park, Salt Lake City, 1, 2 Libya, 1, 2, 3, 4 Light, John, 1 Linguistic Intergroup Bias (LIB) test, 1 Liverpool, 1, 2 Livingstone, Ken, 1, 2 Loja, Angel, 1 London: author’s experience of attack, 1; Copeland nail bombing, 1, 2; Duggan shooting, 1; far-right hate, 1; group threat, 1, 2, 3; online hate speech, 1, 2; Rigby attack, 1; terror attacks, 1, 2, 3, 4, 5, 6 London Bridge attack, 1, 2, 3 London School of Economics, 1 ‘lone wolf’ terrorists, 1, 2, 3, 4 long-term memory, 1, 2, 3, 4 Loomer, Laura, 1 Los Angeles, 1 loss: group threat, 1; subcultures of hate, 1, 2, 3, 4; tipping point, 1; trauma and containment, 1, 2, 3, 4, 5 love, 1, 2 Love Thy Neighbour, 1 Lucero, Marcelo, 1, 2 Luqman, Shehzad, 1 ‘Macbeth effect’, 1 machine learning, 1 Madasani, Alok, 1, 2, 3 Madrid attack, 1, 2 Magnetic Resonance Imaging (MRI): Diffusion MRI, 1, 2; functional MRI, 1, 2, 3, 4, 5, 6, 7 magnetoencephalography (MEG), 1, 2, 3 Maldon, 1 Malik, Tashfeen, 1 Maltby, Robert, 1, 2 Manchester, 1, 2 Manchester Arena attack, 1, 2, 3, 4, 5, 6 marginalisation, 1, 2 Martin, David, 1 Martin, Trayvon, 1, 2 MartinLutherKing.org, 1, 2 martyrdom, 1, 2, 3, 4n masculinity, 1, 2, 3, 4, 5 The Matrix, 1 Matthew Shepard and James Byrd Jr Hate Crimes Prevention Act, 1n, 2n Matz, Sandra, 1 Mauritius, 1 McCain, John, 1 McDade, Tony, 1 McDevitt, Jack, Levin McKinney, Aaron, 1 McMichael, Gregory, 1 McMichael, Travis, 1 media: far-right hate, 1, 2; group threat, 1, 2, 3; steps to stop hate, 1, 2, 3, 4, 5, 6; stereotypes in, 1, 2; subcultures of hate, 1; trigger events, 1 Meechan, Mark, 1 MEG (magnetoencephalography), 1, 2, 3 memory, 1, 2, 3, 4, 5, 6, 7 men, and online hate speech, 1 men’s rights, 1 mental illness, 1, 2, 3, 4, 5, 6 mentalising, 1, 2, 3 meta-analysis, 1 Metropolitan Police, 1 Mexican people, 1, 2, 3, 4 micro-aggressions, 1, 2n, 3, 4, 5, 6 micro-events, 1 Microsemi, 1n Microsoft, 1, 2, 3, 4, 5, 6 micro-targeting, 1, 2 Middle East, 1, 2 migration, 1, 2, 3, 4, 5, 6, 7; see also immigration Milgram, Stanley, 1 military, 1 millennials, 1 Milligan, Spike, 1 Milwaukee, 1, 2, 3 minimal groups, 1 Minneapolis, 1, 2, 3 minority groups: far-right hate, 1, 2; group threat, 1, 2, 3, 4, 5; police reporting, 1; questioning prejudgements, 1; trauma and containment, 1; trigger events, 1, 2 misinformation, 1, 2, 3, 4, 5, 6 mission haters, 1, 2, 3 mobile phones, 1, 2, 3 moderation of content, 1, 2, 3 Moore, Nik, 1 Moore, Thomas, 1 Moores, Manizhah, 1 Moore’s Ford lynching, 1 Moradi, Dr Zargol, 1, 2, 3, 4, 5, 6 Moral Choice Dilemma tasks, 1, 2, 3 moral cleansing, 1, 2, 3 moral dimension, 1, 2, 3, 4 moral outrage, 1, 2, 3, 4, 5 Moroccan people, 1, 2 mortality, 1, 2, 3 mortality salience, 1, 2, 3, 4, 5 Moscow, 1 mosques, 1, 2, 3, 4, 5, 6, 7 Moss Side Blood, 1 mothers, 1, 2, 3, 4, 5, 6 motivation, 1n, 2, 3, 4, 5, 6 Mphiti, Thato, 1 MRI, see Magnetic Resonance Imaging Muamba, Fabrice, 1 multiculturalism, 1, 2, 3, 4 murder: brain injury, 1, 2; group threat, 1, 2, 3; hate counts, 1; identity fusion and hateful murder, 1; police and hate, 1, 2; profiling the hater, 1; trauma and containment, 1, 2, 3, 4, 5 Murdered for Being Different, 1 music, 1, 2, 3 Muslims: COVID-19 pandemic, 1; far-right hate, 1, 2, 3, 4; Google searches, 1; group threat, 1, 2, 3, 4, 5, 6; negative stereotypes, 1; online hate speech, 1, 2; profiling the hater, 1, 2; Salah effect, 1; subcultures of hate, 1, 2, 3; trigger events, 1, 2, 3, 4, 5; and Trump, 1, 2, 3, 4n, 5, 6n Mvubu, Themba, 1 Myanmar, 1, 2 Myatt, David, 1 Nandi, Dr Alita, 1 National Action, 1 National Consortium for the Study of Terrorism and Responses to Terrorism, 1 national crime victimisation surveys, 1, 2 National Front, 1, 2, 3 nationalism, 1, 2 National Socialist Movement, 1, 2, 3, 4 natural experiments, 1, 2 Nature: Neuroscience, 1 nature vs nurture debate, 1 Nazism, 1, 2, 3, 4, 5, 6, 7, 8 NCVS (National Crime Victimisation Survey), 1, 2 negative stereotypes: brain and hate, 1, 2; feeling hate together, 1, 2; group threat, 1, 2, 3, 4, 5, 6; steps to stop hate, 1, 2, 3, 4, 5; tipping point, 1 Nehlen, Paul, 1 neo-Nazis, 1n, 2, 3, 4, 5, 6 Netherlands, 1, 2 Netzwerkdurchsetzungsgesetz (NetzDG) law, 1 neuroimaging, see brain imaging neurons, 1, 2, 3, 4, 5, 6, 7 neuroscience, 1, 2, 3, 4, 5, 6, 7, 8, 9 Newark, 1, 2 news, 1, 2, 3, 4, 5, 6, 7 newspapers, 1, 2, 3, 4 New York City, 1, 2, 3, 4, 5, 6 New York Police Department (NYPD), 1 New York Times, 1, 2 New Zealand, 1 n-grams, 1 Nimmo, John, 1 9/11 attacks, 1, 2, 3, 4, 5, 6, 7 911 emergency calls, 1 Nogwaza, Noxolo, 1 non-independence error, 1, 2n Al Noor Mosque, Christchurch, 1 Northern Ireland, 1 NWA, 1 NYPD (New York Police Department), 1 Obama, Barack, 1n, 2, 3, 4, 5, 6 Occupy Paedophilia, 1 ODIHR, see Office for Democratic Institutions and Human Rights Ofcom, 1 offence, 1, 2, 3, 4 Office for Democratic Institutions and Human Rights (ODIHR), 1, 2 Office for Security and Counter Terrorism, 1 office workers, 1 offline harm, 1, 2 Oklahoma City, 1 O’Mahoney, Bernard, 1 online hate speech: author’s experience, 1; COVID-19 pandemic, 1; far-right hate, 1, 2, 3, 4, 5; hate speech harm, 1; how much online hate speech, 1; individual’s role, 1; law’s role, 1; social media companies’ role, 1; steps to stop hate, 1; tipping point, 1, 2; training the machine to count hate, 1; trigger events, 1 Ono, Kazuya, 1 optical illusions, 1 Organization for Human Brain Mapping conference, 1 Orlando attack, 1 Orwell, George, Nineteen Eighty-Four, 1 Osborne, Darren, 1 ‘other’, 1, 2, 3, 4, 5, 6 Ottoman Empire, 1 outgroup: author’s brain and hate, 1, 2, 3; brain and hate, 1, 2, 3, 4, 5, 6, 7; child interaction and play, 1, 2; evolution of group threat detection, 1; feeling hate together, 1; group threat, 1, 2, 3, 4, 5, 6; ‘gut-deep’ hate, 1; HateLab Brexit study, 1; human biology and threat, 1; identity fusion, 1; prejudice formation, 1; profiling the hater, 1; push/pull factor, 1; pyramid of hate, 1; society, competition and threat, 1; steps to stop hate, 1, 2; tipping point, 1; trauma and containment, 1, 2, 3, 4, 5; trigger events, 1, 2, 3, 4, 5, 6, 7, 8 outliers, 1 Overton window, 1, 2, 3, 4 oxytocin, 1, 2, 3, 4 Paddock, Stephen, 1 Paddy’s Pub, Bali, 1 paedophilia, 1, 2, 3, 4, 5 page rank, 1 pain, 1, 2, 3, 4, 5, 6, 7 Pakistani people, 1, 2, 3, 4, 5 Palestine, 1 pandemics, 1, 2, 3, 4 Papua New Guinea, 1, 2, 3 paranoid schizophrenia, 1, 2 parents: caregiving, 1; subcultures of hate, 1; trauma and containment, 1, 2, 3, 4, 5; trigger events, 1, 2, 3 Paris attack, 1 Parsons Green attack, 1, 2 past experience: the ‘average’ hate criminal, 1; the ‘exceptional’ hate criminal, 1; trauma and containment, 1 perception-based hate crime, 1, 2 perception of threat, 1, 2, 3, 4, 5 perpetrators, 1, 2 personal contact, 1, 2 personality, 1, 2, 3 personality disorder, 1, 2 personal safety, 1, 2 personal significance, 1 perspective taking, 1, 2 PFC, see prefrontal cortex Philadelphia Police Department, 1 Philippines, 1 physical attacks, 1, 2, 3, 4, 5, 6, 7, 8 play, 1 Poland, 1, 2, 3 polarisation, 1, 2, 3, 4, 5 police: brain and hate, 1, 2; Duggan shooting, 1; group threat, 1, 2, 3; and hate, 1; NYPD racial bias, 1; online hate speech, 1, 2, 3, 4; perceiving versus proving hate, 1; police brutality, 1, 2, 3, 4; predicting hate crime, 1; recording crime, 1, 2, 3, 4; reporting crime, 1, 2, 3; rising hate count, 1, 2, 3; ‘signal’ hate acts and criminalisation, 1; steps to stop hate, 1, 2, 3; use of force, 1 Polish migrants, 1 politics: early adulthood, 1; far-right hate, 1, 2; filter bubbles and bias, 1; group threat, 1, 2, 3; online hate speech, 1, 2; seven steps to stop hate, 1, 2, 3, 4; trauma and containment, 1; trigger events, 1, 2, 3, 4, 5; Trump election, 1, 2 populism, 1, 2, 3, 4, 5 pornography, 1 Portugal, 1, 2 positive stereotypes, 1, 2 post-traumatic stress disorder (PTSD), 1, 2, 3, 4, 5 poverty, 1, 2, 3 Poway synagogue shooting, 1 power, 1, 2, 3, 4, 5 power law, 1 predicting the next hate crime, 1 prefrontal cortex (PFC): brain and signs of prejudice, 1; brain injury, 1; disengaging the amygdala autopilot, 1; feeling pain, 1; ‘gut-deep’ hate, 1; prejudice network, 1; psychological brainwashing, 1; recognising false alarms, 1; salience network, 1; trauma and containment, 1; trigger events, 1; unlearning prejudiced threat detection, 1, 2 prehistoric brain, 1, 2 prehistory, 1, 2 prejudgements, 1 prejudice: algorithms, 1; author’s brain and hate, 1, 2, 3, 4, 5, 6, 7; brain and hate, 1, 2, 3, 4, 5, 6, 7; brain and signs of prejudice, 1; cultural machine, 1; far-right hate, 1, 2; filter bubbles and bias, 1; foundations of, 1; Google, 2; group threat, 1, 2, 3, 4, 5, 6, 7, 8, 9; human biology and threat, 1; neuroscience of hate, 1, 2; online hate speech, 1, 2, 3; parts that process prejudice, 1; prejudice network, 1, 2, 3, 4; prepared versus learned amygdala responses, 1; pyramid of hate, 1; releasers, 1, 2; steps to stop hate, 1, 2, 3, 4; tipping point from prejudice to hate, 1; trauma and containment, 1, 2, 3, 4, 5; trigger events, 1, 2, 3, 4, 5, 6, 7, 8; Trump, 1, 2; unconscious bias, 1; unlearning prejudiced threat detection, 1; what it means to hate, 1, 2, 3, 4, 5 prepared fears, 1, 2 Prisoner’s Dilemma, 1 profiling the hater, 1 Proposition 1, 2 ProPublica, 1n, 2 prosecution, 1, 2, 3 Protestants, 1 protons, 1 psychoanalysis, 1 psychological development, 1, 2, 3, 4 psychological profiles, 1 psychological training, 1 psychology, 1, 2, 3, 4 psychosocial criminology, 1, 2 psy-ops (psychological operations), 1 PTSD, see post-traumatic stress disorder Public Order Act, 1 pull factor, 1, 2, 3, 4, 5 Pullin, Rhys, 1n Purinton, Adam, 1, 2, 3, 4, 5, 6, 7 push/pull factor, 1, 2, 3, 4, 5, 6 pyramid of hate, 1, 2 Q …, 1 al-Qaeda, 1, 2 quality of life, 1 queer people, 1, 2 quest for significance, 1, 2, 3 Quran burning, 1 race: author’s brain and hate, 1, 2, 3, 4; brain and hate, 1, 2, 3, 4, 5, 6, 7; brain and signs of prejudice, 1; far-right hate, 1, 2, 3; Google searches, 1; group threat, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10; hate counts, 1, 2, 3; online hate speech, 1; predicting hate crime, 1; pyramid of hate, 1; race relations, 1, 2, 3; race riots, 1, 2; race war, 1, 2, 3, 4, 5; steps to stop hate, 1, 2, 3; trauma and containment, 1, 2, 3, 4n, 5, 6; trigger events, 1, 2; unconscious bias, 1; unlearning prejudiced threat detection, 1 racism: author’s experience, 1; brain and hate, 1, 2, 3, 4, 5, 6; far-right hate, 1, 2; group threat, 1, 2, 3, 4, 5, 6, 7, 8; Kansas shooting, 1; NYPD racial bias, 1; online hate speech, 1, 2, 3, 4; steps to stop hate, 1n, 2, 3; Tay chatbot, 1; trauma and containment, 1, 2, 3, 4, 5, 6, 7; Trump election, 1; victim perception of motivation, 1n; white flight, 1 radicalisation: far-right hate, 1, 2, 3; group threat, 1; subcultures of hate, 1, 2, 3, 4, 5; trigger events, 1 rallies, 1, 2, 3; see also Charlottesville rally Ramadan, 1, 2 rape, 1, 2, 3, 4, 5 rap music, 1 realistic threats, 1, 2, 3, 4, 5 Rebel Media, 1 rebels, 1 recategorisation, 1 recession, 1, 2, 3, 4, 5 recommendation algorithms, 1, 2 recording crime, 1, 2, 3, 4 red alert, 1 Reddit, 1, 2, 3, 4 red-pilling, 1, 2, 3, 4 refugees, 1, 2, 3, 4, 5 rejection, 1, 2, 3, 4, 5, 6 releasers of prejudice, 1, 2 religion: group threat, 1, 2, 3; homosexuality, 1; online hate speech, 1, 2, 3; predicting hate crime, 1; pyramid of hate, 1; religion versus hate, 1; steps to stop hate, 1, 2; subcultures of hate, 1, 2; trauma and containment, 1n, 2; trigger events, 1, 2, 3, 4, 5; victim perception of motivation, 1n reporting crimes, 1, 2, 3, 4, 5, 6, 7 repression, 1 Republicans, 1, 2, 3, 4, 5 research studies, 1 responsibility, 1, 2, 3 restorative justice, 1 retaliatory haters, 1, 2, 3 Reuters, 1 Rieder, Bernhard, 1 Rigby, Lee, 1 rights: civil rights, 1, 2, 3, 4; gay rights, 1, 2, 3, 4; human rights, 1, 2, 3; men’s rights, 1; tipping point, 1; women’s rights, 1, 2 right wing, 1, 2, 3, 4, 5, 6; see also far right Right-Wing Authoritarianism (RWA) scale, 1 riots, 1, 2, 3, 4 risk, 1, 2, 3 rites of passage, 1, 2 rituals, 1, 2, 3 Robb, Thomas, 1 Robbers Cave Experiment, 1, 2, 3, 4, 5, 6 Robinson, Tommy (Stephen Yaxley-Lennon), 1, 2, 3, 4 Rohingya Muslims, 1, 2 Roof, Dylann, 1, 2 Roussos, Saffi, 1 Rudolph, Eric, 1 Rushin, S,, 1n Russia, 1, 2, 3, 4, 5, 6, 7, 8 Russian Internet Research Agency, 1 RWA (Right-Wing Authoritarianism) scale, 1 Rwanda, 1 sacred value protection, 1, 2, 3, 4, 5, 6, 7, 8 Saddam Hussein, 1 safety, 1, 2 Sagamihara care home, Japan, 1, 2 Salah, Mohamed, 1, 2, 3 salience network, 1, 2 salmon, brain imaging of, 1 Salt Lake City, 1 same-sex marriage, 1, 2 same-sex relations, 1, 2, 3 San Bernardino attack, 1n, 2, 3 Scanlon, Patsy, 1 scans, see brain imaging Scavino, Dan, 1n schizophrenia, 1, 2, 3, 4 school shootings, 1, 2 science, 1, 2, 3 scripture, 1, 2 SDO, see Social Dominance Orientation (SDO) scale Search Engine Manipulation Effect (SEME), 1 search queries, 1, 2, 3, 4 Second World War, 1, 2, 3 Section 1, Local Government Act, 1, 2, 3 seed thoughts, 1 segregation, 1, 2, 3 seizures, 1, 2, 3 selection bias problem, 1n self-defence, 1, 2 self-esteem, 1, 2, 3, 4 self-sacrifice, 1, 2, 3 Senior, Eve, 1 serial killers, 1, 2, 3 7/7 attack, London, 1 seven steps to stop hate, 1; becoming hate incident first responders, 1; bursting our filter bubbles, 1; contact with others, 1; not allowing divisive events to get the better of us, 1; overview, 1; putting ourselves in the shoes of ‘others’, 1; questioning prejudgements, 1; recognising false alarms, 1 sexism, 1, 2 sexual orientation, 1, 2, 3, 4, 5, 6, 7 sexual violence, 1, 2, 3, 4, 5 sex workers, 1, 2, 3, 4 Shakespeare, William, Macbeth, 1 shame, 1, 2, 3, 4, 5, 6, 7, 8, 9 shared trauma, 1, 2, 3 sharia, 1, 2 Shepard, Matthew, 1, 2 Sherif, Muzafer, 1, 2, 3, 4, 5, 6, 7 shitposting, 1, 2, 3n shootings, 1, 2, 3, 4, 5, 6, 7, 8 ‘signal’ hate acts, 1 significance, 1, 2, 3 Simelane, Eudy, 1 skin colour, 1, 2, 3n, 4, 5, 6, 7 Skitka, Linda, 1, 2 slavery, 1 Slipknot, 1 slurs, 1, 2, 3, 4, 5, 6 Snapchat, 1 social class, 1, 2 social desirability bias, 1, 2 Social Dominance Orientation (SDO) scale, 1 social engineering, 1 socialisation, 1, 2, 3, 4, 5 socialism, 1, 2 social media: chatbots, 1; COVID-19 pandemic, 1; far-right hate, 1, 2, 3, 4; filter bubbles and bias, 1; HateLab Brexit study, 1; online hate speech, 1, 2, 3, 4, 5; online news, 1; pyramid of hate, 1; steps to stop hate, 1, 2, 3; subcultures of hate, 1; trigger events, 1, 2; see also Facebook; Twitter; YouTube Social Perception and Evaluation Lab, 1 Soho, 1 soldiers, 1n, 2, 3 Sorley, Isabella, 1 South Africa, 1 South Carolina, 1 Southern Poverty Law Center, 1n, 2 South Ossetians, 1 Soviet Union, 1, 2 Spain, 1, 2, 3 Spencer, Richard B., 1 Spengler, Andrew, 1, 2, 3, 4 SQUIDs, see superconducting quantum interference devices Stacey, Liam, 1, 2 Stanford University, 1 Star Trek, 1, 2, 3 statistics, 1, 2, 3, 4, 5, 6, 7, 8 statues, 1 Stephan, Cookie, 1, 2 Stephan, Walter, 1, 2 Stephens-Davidowitz, Seth, Everybody Lies, 1 Stereotype Content Model, 1 stereotypes: brain and hate, 1, 2, 3, 4, 5, 6, 7; cultural machine, group threat and stereotypes, 1; definitions, 1; feeling hate together, 1, 2; group threat, 1, 2, 3, 4; homosexuality, 1; NYPD racial bias, 1; steps to stop hate, 1, 2, 3, 4, 5; study of prejudice, 1; tipping point, 1; trigger events, 1 Stoke-on-Trent, 1, 2 Stormfront website, 1, 2, 3 storytelling, 1 stress, 1, 2, 3, 4, 5, 6, 7, 8 striatum, 1, 2, 3n, 4 subcultures, 1, 2, 3, 4, 5 subcultures of hate, 1; collective quests for significance and extreme hate, 1; extremist ideology and compassion, 1; fusion and generosity towards the group, 1; fusion and hateful murder, 1; fusion and hateful violence, 1; fusion and self-sacrifice in the name of hate, 1; quest for significance and extreme hatred, 1; religion/belief, 1; warrior psychology, 1 subhuman, 1, 2 Sue, D.

Reinventing Organizations: A Guide to Creating Organizations Inspired by the Next Stage of Human Consciousness
by Frederic Laloux and Ken Wilber
Published 9 Feb 2014

The “firms of endearment” studied by the authors obtained a cumulative return to shareholders of 1,025 percent over the 10 years leading up to the research, as compared to 122 percent for the S&P 500. From a methodological point of view, these results should be taken with a grain of salt. There is an obvious selection bias, as only exceptional companies that one would expect to outperform their peers were handpicked into the sample. The benchmark of the S&P 500 wasn’t adjusted for industry, size, or other criteria. Furthermore, criteria other than the organization model, such as patents, innovative business models, and asset utilizations that could explain the superior result, were not filtered out.

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Chapter 3.3: Transforming an existing organization 135 Bakke, Joy at Work, 176-177. 136 Zobrist, La belle histoire de FAVI, 38. 137 Anthony S. Bryk and Barbara Schneider, Trust in Schools: A Core Resource for School Reform (New York : Russell Sage Foundation, 2002). Chapter 3.4: Results 138 Of course, we should be careful about the possibility of a selection bias. While I have researched all the organizations I have found that corresponded to the research criteria (more than 100 employees, operating for at least five years on principles and practices inspired to some significant degree by the Evolutionary-Teal paradigm), it could well be that only particularly success-ful organizations caught my attention. 139 For instance A.

Success and Luck: Good Fortune and the Myth of Meritocracy
by Robert H. Frank
Published 31 Mar 2016

Gladwell, Outliers, chap. 1. 16. Some authors have suggested that the success of players born earlier in the year results less from the fact that they are actually better than from the fact that NHL teams perceive them as better. See, for example, Robert O. Deaner, Aaron Lowen, and Stephen Cobley, “Born at the Wrong Time: Selection Bias in the NHL Draft,” PLOS One, February 27, 2013, http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0057753. But even if this is true, Gladwell’s claim still holds that an aspiring hockey star is lucky to have been born earlier in the year. 17. Elizabeth Dhuey and Stephen Lipscomb, “What Makes a Leader?

The Extended Phenotype: The Long Reach of the Gene
by Richard Dawkins
Published 1 Jan 1982

If, on the other hand, the difference in survival consequences between a putative replicator and its alleles is almost negligible, the replicators under discussion would have to be quite small if the difference in their survival values is to make itself felt. This is the rationale behind Williams’s (1966, p. 25) definition: ‘In evolutionary theory, a gene could be defined as any hereditary information for which there is a favorable or unfavorable selection bias equal to several or many times its rate of endogenous change.’ The possibility of strong linkage disequilibrium (Clegg 1978) does not weaken the case. It simply increases the size of the chunk of genome that we can usefully treat as a replicator. If, which seems doubtful, linkage disequilibrium is so strong that populations contain ‘only a few gametic types’ (Lewontin 1974, p. 312), the effective replicator will be a very large chunk of DNA.

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Molecular biologists usually employ it in the sense of cistron (q.v.). Population biologists sometimes use it in a more abstract sense. Following Williams (1966, p. 24), I sometimes use the term gene to mean ‘that which segregates and recombines with appreciable frequency’, and (p. 25) as ‘any hereditary information for which there is a favorable or unfavorable selection bias equal to several or many times its rate of endogenous change’. gene-pool The whole set of genes in a breeding population. The metaphor on which the term is based is a happy one for this book, for it de-emphasizes the undeniable fact that genes actually go about in discrete bodies, and emphasizes the idea of genes flowing about the world like a liquid.

Model Thinker: What You Need to Know to Make Data Work for You
by Scott E. Page
Published 27 Nov 2018

The model predicts that a student who spends seven hours studying and takes one other accelerated class should score in the 90s. The model can also guide actions, though we must be cautious, as we cannot infer causality. The data show that students who study and take accelerated classes perform better. One reason studying more or taking those classes may not help is selection bias. It might be that the students who study more and those who take accelerated classes are better at math. Even though regressions cannot prove what causes patterns in data, they can rule out explanations. Take the large wealth disparity by race in the United States: in 2016, the average wealth of white families (approximately $110,000) was more than ten times that of African American and Latino families.

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Schelling, Thomas, 184, 216 Schelling’s party model, 216 defining, 217 Schelling’s segregation model, 353 relocations in, 220 (fig.) tolerance threshold in, 219 seats, power and, 113 (fig.) second-price auction, 288 segregation models of, 216–220 production of, 217 (fig.) See also Schelling’s segregation model selection bias, 89 self-organization, 75, 184–186 self-organized criticality model, 74 self-organizing activities model, 185 separability, many-model thinking and, 11–12 separation, 298, 299 with continuous signals, 301 sequential games, 246–247 sets testing, 87 training, 87 Shapley, Lloyd, 110 Shapley value, 107, 108 alternative uses test and, 111–112, 112 (fig.)

Aftermath: Following the Bloodshed of America's Wars in the Muslim World
by Nir Rosen
Published 21 Apr 2011

They lacked the curiosity to understand other cultures and the empathy to understand what motivated other people. In the military in particular, Afghans were still viewed as “hajjis.” Alternative viewpoints were not considered. Many journalists failed to understand that when you’re with the military you’re changing your selection bias. By showing up with the white guys with guns, you are eliminating all the people who don’t want to talk to the military, or talking to those who have an interest in engaging the foreign occupier. Regular people won’t relate to you in a natural or honest way. For the U.S. military, seeing something from a reporter’s or Afghan’s perspective is an exception.

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McCain, John McChrystal, Stanley McCollough, William McDonalds McFarland, Sean McGurk, Brett McKiernan, David McMullen, Chris Mecca Media in Afghanistan Arab, resentment toward and the battle of Nahr al-Barid camp and the COIN manual conduct of the embedding with the American military, criticism of freedom of the, issue of holed up in Lebanon importance of the, realizing the kidnapping involving the killing of a member of the Lebanese, blaming the local Lebanese attitude towards perceived as the enemy Saudis use of the and selection bias and the surge U.S., focus of the, since the beginning of the occupation, issue with Media blackout Medical City Medina al-Meis, Khalil Meshal, Khalid Mesopotamia, capture of Mexico Middle East euphemism for American-backed militias in the failed U.S. policies in, effect of identity politics in the, resurgence of impact on the largest refugee crisis in the perceived Jewish and Zionist strategy in the perception of Al Qaeda and Americans in the Shiite vs.

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See Abu Risha, Sheikh Sattar Satterfield, David Saudi Arabia and Afghanistan and Al Qaeda aligning with Jordan apprehension of and Fatah al-Islam ignoring involvement of interference and money from, effect of and Lebanon and the March 14 coalition regional rivalry between Iran and sectarianism in Saudi proxies Sayyaf, Abdul Rasul al-Sayyid, Ridwan Schools ban on attending religious, in Pakistan teaching jihad ideas in Schwarzenegger, Arnold Secret prisons Sectarian cleansing See also Iraqi civil war Sectarianism artificial, in Lebanon and Basra blaming Americans for continued slaughter due to, aspects of the degree of, diminished descent into dividing the militias, effect of in the Education Ministry in Egypt elections that enshrined frustration over, potential for continued in the government history of and identity politics of the Iraqi Security Forces and Jordan and Lebanon as more covert overt, receding of perceptions based on in the police force provoking in the regional, possibility of regional, rise in rise in and Saddam sign of shift away from during the surge Syria and in universities towards students U.S. occupation promoting See also specific geographical areas Sects, religious. See specific sects Secure Plus security company Seidiya Guard Selection bias Sepp, Kalev September 11 attacks Sermons, impact of Shaab Shab-e-Barat holiday Shah of Iran al-Shahal, Sheikh Dai al-Islam Shallaq, Fadil Sham See also Jund al-Sham (Soldiers of Sham/Levant) al-Shami, Abu Anas Shanshal, Falah Hassan Shaqis, recruitment of Sharikat al-Sadr (Rays of Sadr) newspaper al-Sharman, Muhamad Mahmud Sharon, Ariel Sharqiya television Shatila refugee camp Shawish, Zuheir Shawkat, Asef Shehab, Fouad al-Sheikh, Fattah “Shiite crescent,” Shiite Hizballah-Iranian model Shiite House Shiite militias accusations against in Amriya cease-fire of depending on Al Qaeda for protection from and ease of integration into the ISF Hizballah training, media accusation of linked to Iraqi Security Forces other targets of and the road to civil war treatment of Palestinians See also specific militia groups and leaders Shiite mosques See also specific mosques Shiite pilgrims Shiite Political Council Shiite revival Shiite shrines See also specific shrines “Shiite south” label Shiites actual representation of Sunnis and American perception of beliefs held by common epithet involving divided, and U.S. strategic interests empowerment of failed uprising against Saddam by in the first outbreaks of civil war as the first target of AQI important holidays of moderate, losing and mosque attendance new Saddam of the periphery vs. center preferred burial site for reported missing on the Internet Salafi view of secular as the winners Zarqawi’s warning to See also specific Shiite leaders/people and organizations Shiite-Sunni conflict/violence.

The Purpose Economy: How Your Desire for Impact, Personal Growth and Community Is Changing the World
by Aaron Hurst
Published 31 Aug 2013

The future of personal and small business finance is social. By making banking social again and using technology to make it efficient, we can not only boost demand, but also increase the efficiency and reliability of underwriting and collections. By involving the community in deciding who gets a loan, it creates a selection bias, as people are more self-aware about the debt they should be taking on when it is transparent to their community. And when people’s repayments impact the ability of their friends and family to borrow, they are much more likely to make their payments. Lenddo now focuses on lending to the middle class in developing countries, a population that is under-banked but also lives in regions with flexible government regulations that enable the innovation.

Future Politics: Living Together in a World Transformed by Tech
by Jamie Susskind
Published 3 Sep 2018

So let’s take a step back and look at the bigger picture. There are two main categories of algorithmic injustice: databased injustice and rule-based injustice. Data-Based Injustice Injustice can occur when an algorithm is applied to data that is poorly selected, incomplete, outdated, or subject to selection bias.1 Bad data is a particular problem for machine learning algorithms, which can only ‘learn’ from the data to which they are applied. Algorithms trained to identify human faces, for instance, will struggle or fail to recognize the faces of non-white people if they are trained using majority-white faces.2 Voice-recognition algorithms will not ‘hear’ women’s voices if they are trained from datasets with too many male voices.3 Even an algorithm trained to judge human beauty on the basis of allegedly ‘neutral’ characteristics like facial symmetry, wrinkles, and youthfulness will develop a taste for Caucasian features if it is trained using mostly white faces.

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Just one had visibly dark skin.4 The image-hosting website Flickr autotagged photographs of black people as ‘animal’ and ‘ape’ and pictures of concentration camps as ‘sport’ and ‘jungle gym’.5 Google’s Photos algorithm tagged two black people as ‘Gorillas’.6 No matter how smart an algorithm is, if it is fed a partial or misleading view of the world it will treat unjustly those who have been hidden from its view or presented in an unfair light. This is data-based injustice. OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Algorithmic Injustice 283 Rule-Based Injustice Even when the data is not poorly selected, incomplete, outdated, or subject to selection bias, injustice can result when an algorithm applies unjust rules. There are two types: those that are overtly unjust, and those that are implicitly unjust. Overtly Unjust Rules An overtly unjust rule is one that is used to decide questions of distribution and recognition according to criteria that (in that context) are unjust on their very face.

Mastering the VC Game: A Venture Capital Insider Reveals How to Get From Start-Up to IPO on Your Terms
by Jeffrey Bussgang
Published 31 Mar 2010

But it should result in creating more sustainable, valuable, and important companies at the end of the development cycle. Venture-backed start-ups have something magic about them. Whether it’s because of the discipline that an outside investor imposes on a start-up, the value and experience that VCs bring to the table, or simply a selection bias, venture-backed start-ups outperform all other forms of entrepreneurial ventures. “With forty or fifty years of venture capital investing, why are these companies still growing jobs at twice the rate of the other members of the private sector?” asks Polaris’s McGuire. “You have to go back to the core.

Different: Escaping the Competitive Herd
by Youngme Moon
Published 5 Apr 2010

On the other hand, this is why I believe these brands are deserving of study: Because they are, at the very least, trying to play the story forward in a different way. They are trying to envision the market through the lens of alternative future possibility. Of course, the problem with pointing to certain brands as positive examples of anything is that there is an obvious selection bias. Great brands are experiential and personal, which means that the only legitimate arbiter of whether a brand is great is anyone who happens to experience it. Put another way, I’m no more an expert on what makes a brand praiseworthy than you are, and it would be hubris for me to presume otherwise.

Swimming With Sharks: My Journey into the World of the Bankers
by Joris Luyendijk
Published 14 Sep 2015

Criticism of this sort appeared under virtually every interview, always from people presenting themselves as insiders and often accompanied by the suggestion that the blog was a cheap attempt by the Guardian to score points with lefty readers. Obviously, I was concerned about the reliability and representativeness of the sort of people who risk their job for an interview – ‘selection bias’ in social science-speak. Whether interviewees were who they said they were was quite easy to check on social media such as LinkedIn. Verifying their stories was a different matter, frustratingly, because I was not allowed to observe anyone working in the banks. However, the most important things in their stories could be substantiated: the existence of caveat emptor, zero job security, the dangerous logic of ‘too big to fail’ and the implications and pressures of a listing on the stock exchange.

The Antidote: Happiness for People Who Can't Stand Positive Thinking
by Oliver Burkeman
Published 1 Jul 2012

Steven Pritzker and Mark Runco (Waltham, Massachusetts: Academic Press, 1999): 1379-84; available at utsc.utoronto.ca/~dunbarlab/pubpdfs/DunbarCreativityEncyc99.pdf ‘If you’re a scientist and you’re doing an experiment’: From a PopTech conference talk by Kevin Dunbar, ‘Kevin Dunbar on Unexpected Science’, accessible online at poptech.org/popcasts/kevin_dunbar_on_unexpected_science As he told the neuroscience writer Jonah Lehrer: See Jonah Lehrer, ‘Accept Defeat: The Neuroscience of Screwing Up’, Wired, January 2010. ‘Think about it’: All quotations from Jerker Denrell are from my interview with him or from Jerker Denrell, ‘Vicarious Learning, Undersampling of Failure, and the Myths of Management’, Organization Science 2003 (14): 227-43; and Jerker Denrell, ‘Selection Bias and the Perils of Benchmarking’, Harvard Business Review, April 2005. research into media commentators who make predictions: Jerker Denrell and Christina Fang, ‘Predicting the Next Big Thing: Success as a Signal of Poor Judgment’, Management Science 56 (2010): 1653-67; see also Joe Keohane, ‘That Guy Who Called the Big One?

The Metric Society: On the Quantification of the Social
by Steffen Mau
Published 12 Jun 2017

The platforms in question invite students to rate their courses and tutors, in order – as they claim – to provide useful feedback for staff while at the same time helping fellow students to choose the right course. Whether these platforms are in fact suitable as a source of reliable information on teaching quality is much disputed, however (cf. Otto et al. 2008; Silva et al. 2008). Especially critical, say detractors, is the selection bias, as students with strong views (whether positive or negative) are more likely to express them. Moreover, online platforms are presumably affected even more than classroom surveys by what is known in psychology as the halo effect, meaning a cognitive bias that emphasizes certain favourable characteristics of a person while glossing over others.

Strength in Numbers: How Polls Work and Why We Need Them
by G. Elliott Morris
Published 11 Jul 2022

After deciding he didn’t want to be a lawyer, he went into academia. In 1981, he graduated with his PhD from Harvard and joined its faculty as an assistant professor. The econometrics field at that time was exploding with new methods of empirical research. He got to study measurement error and selection bias—two things that “really agreed with” him—and had unearthed many applications to political science. “I was a kid in a candy store” for shopping around for more methods, he told me.7 Rivers went on to teach at the California Institute of Technology, the research university in Pasadena, as well as University of California, Los Angeles, and Stanford, where he has been a professor since 1989.

Empirical Market Microstructure: The Institutions, Economics and Econometrics of Securities Trading
by Joel Hasbrouck
Published 4 Jan 2007

The results might then be used to guide customers in directing their orders and other aspects of trading strategy. One might, for example, use regression estimates to project the execution cost of a contemplated order strategy. Although these sorts of analyses are sensible first steps, they are based on samples that are generated with a selection bias. The source of this bias lies in the decisions of the traders who submitted the orders. Corrections to deal with this bias have been implemented by Madhavan and Cheng (1997) and Bessembinder (2004). 15 Prospective Trading Costs and Execution Strategies In this chapter we discuss minimization of expected implementation cost in two stylized dynamic trading problems.

The Age of the Infovore: Succeeding in the Information Economy
by Tyler Cowen
Published 25 May 2010

For the most part it fell upon a stony silence and many people don’t believe that someone as successful as Vernon could possibly be autistic. Often outsiders don’t see the cognitive strengths along the autism spectrum because they focus excessively on what is highly or easily visible. Autism in the modern world is often about “diagnosis” and “treatment,” and that creates a selection bias. Medical professionals control the familiar definitions of autism and they meet those people or parents who come to them for help. It’s no surprise that these people and their doctors are focused on life problems. At the same time, many of the autistics with relatively high social status don’t want to affiliate with the concept or, more frequently, they are genuinely unaware that they might qualify as autistic in some manner.

Hello World: Being Human in the Age of Algorithms
by Hannah Fry
Published 17 Sep 2018

Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age (Chicago and London: University of Chicago Press, 2007), p. 1. 20. Philip Howard, Brian Francis, Keith Soothill and Les Humphreys, OGRS 3: The Revised Offender Group Reconviction Scale, Research Summary 7/09 (London: Ministry of Justice, 2009), https://core.ac.uk/download/pdf/1556521.pdf. 21. A slight caveat here: there probably is some selection bias in this statistic. ‘Ask the audience’ was typically used in the early rounds of the game, when the questions were a lot easier. None the less, the idea of the collective opinions of a group being more accurate than those of any individual is a well-documented phenomenon. For more on this, see James Surowiecki, The Wisdom of Crowds: Why the Many Are Smarter than the Few (New York: Doubleday, 2004), p. 4. 22.

Transport for Humans: Are We Nearly There Yet?
by Pete Dyson and Rory Sutherland
Published 15 Jan 2021

In the private, public and third sectors we see how quality can be compromised by lack of time and money, by imprecise research questions, and (worst of all) by a motivation for the research to simply tick boxes to support an existing conclusion. Listening to what people think is very important, but doing so should be accompanied by invoking psychological principles and drawing on creative thinking to avoid becoming a funnel through which all ideas must pass to be considered valid. Selection bias Who do you ask for an opinion? We need to observe and listen to people’s problems, focusing on the obstacles and pinch points of everyone affected by transport. And that means searching beyond the existing users, and even trying to find the people who hate the current options. The best social research invests in large samples from nationally representative panels of participants, often with ‘boosts’ in key demographics to enable analysis among marginalized groups.

Escape From Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It
by Erica Thompson
Published 6 Dec 2022

An unpublished study of farmers in Mali who were given access to seasonal weather forecasts showed that they had better outcomes at the end of the farming season than farmers without access to these forecasts, even though those particular forecasts showed very little skill in predicting the actual weather. One reason for this success might be that the farmers were able to frame their decisions more effectively on the basis of risk management, or to make more confident decisions about when and what to plant. I don’t want to make too much of this example, though, since there could be a selection bias here too: for example, if the most financially secure farmers were the ones who made use of the information and those who were already struggling could not manage the additional burden of considering the new input. Thinking about other practices, ideas such as ‘planting by the Moon’ support gardeners by providing a clear framework for planting peas in one week and carrots in another, with the result that the peas and carrots are planted at an approximately sensible time and do not end up left on the shelf or forgotten.

The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses
by Eric Ries
Published 13 Sep 2011

In magazines and newspapers, in blockbuster movies, and on countless blogs, we hear the mantra of the successful entrepreneurs: through determination, brilliance, great timing, and—above all—a great product, you too can achieve fame and fortune. There is a mythmaking industry hard at work to sell us that story, but I have come to believe that the story is false, the product of selection bias and after-the-fact rationalization. In fact, having worked with hundreds of entrepreneurs, I have seen firsthand how often a promising start leads to failure. The grim reality is that most startups fail. Most new products are not successful. Most new ventures do not live up to their potential.

Psychopathy: An Introduction to Biological Findings and Their Implications
by Andrea L. Glenn and Adrian Raine
Published 7 Mar 2014

If rates of antisocial behavior are lower in the offspring of those who reduced their smoking habits during pregnancy, we can conclude that prenatal smoking is causally related to antisocial behavior in offspring. However, a study in which pregnant women are not randomly assigned to groups but choose whether to participate in the program or Prevention, Intervention, and Treatment >> 179 not is subject to self-selection bias, meaning that there may be differences between women who choose to participate and those who do not, and potential effects may be due to these external factors rather than to differences in smoking behavior. Future studies implementing the more rigorous approach of the randomized controlled trial will undoubtedly be beneficial in both furthering our understanding of psychopathy and making progress toward solving the problem.

4th Rock From the Sun: The Story of Mars
by Nicky Jenner
Published 5 Apr 2017

Unfortu­nately, it turns out that we can’t track Mars’s motion through the sky in order to produce a super-athletic brood of children. In 1997, a team of researchers concluded, ‘after persistent and painstaking examination’, that ‘there is insufficient evidence for the “Mars effect” … this effect may be attributed to Gauquelin’s selective bias in either discarding or adding data post hoc. It is time to move on to other more productive topics’. The 1970s saw the arrival of the New Age movement, and with it the now-unavoidable flood of tabloid horoscope columns and television astrologers. These columns, while (hopefully) very rarely regarded as rigorous, draw on our established astrological perceptions of Mars.

Young Money: Inside the Hidden World of Wall Street's Post-Crash Recruits
by Kevin Roose
Published 18 Feb 2014

I brought this up with a nonbanker friend of mine. She winced. “Do you think you’re getting the whole picture?” she asked. “Are you sure you’re not just talking to the ones you find tolerable and ignoring the douchebags?” She had a point: I did have a douchebag deficit, and my project had an inherent selection bias. My banker sources were atypical by definition—daring or disillusioned enough to be willing to risk getting fired by talking to me, and kind and introspective enough to spend hours at a time answering probing questions about their lives. They had all bought in to some portion of the financial world’s value system, but they all considered themselves outsiders.

Humble Pi: A Comedy of Maths Errors
by Matt Parker
Published 7 Mar 2019

It provides a fantastic insight into how email is used within such a large company. And, of course, they were emailing a lot of spreadsheets as attachments. Hermans and her colleagues searched through the email archive and were able to assemble a corpus of 15,770 real-world spreadsheets as well as 68,979 emails pertaining to spreadsheets. There is some selection bias because these spreadsheets were from a company being investigated for poor financial reporting, which is a shame. But it was still an incredible snapshot of how spreadsheets are actually used in the real world, as well as the way in which emails showing those spreadsheets were discussed, passed around and updated.

The Lies That Bind: Rethinking Identity
by Kwame Anthony Appiah
Published 27 Aug 2018

Mendel (1866), “Versuche über Pflanzenhybriden,” Verhandlungen des naturforschenden Vereines in Brünn, Bd. IV für das Jahr 1865, Abhandlungen: 3–47. http://www.biodiversitylibrary.org/item/124139#page/133. 17.Beth Carter, “Want to Play in the NHL? Better Hope You Were Born in the Right Month,” Wired, March 4, 2013, https://www.wired.com/2013/03/nhl-selection-bias/. 18.E. W. Blyden, Sierra Leone Weekly News, May 27, 1893, as cited in Eliezer Ben-Rafael and Yitzhak Sternberg with Judit Bokser Liwerant and Yosef Gorny, Transnationalism: Diasporas and the Advent of a New Disorder (Leiden: Brill, 2000), 598. 19.W. E. B. Du Bois, “To the Nations of the World,” in Lift Every Voice: African American Oratory, 1787–1900, ed.

DIY Investor: How to Take Control of Your Investments & Plan for a Financially Secure Future
by Andy Bell
Published 12 Sep 2013

The impact of the Retail Distribution Review The abolition of commission payable to advisers on retail investment products from 31 December 2012 has caused a revolution in the way unit trusts and OEICs are distributed and priced. The commission ban was introduced under the Retail Distribution Review (RDR), a regulatory-driven initiative to remove product selection bias from the financial advice process and to improve the professional standards of advisers. One of the consequences of the RDR is the creation of a whole new set of commission-free, or ‘clean’, unit or share classes of funds. Before 2013 most retail unit trusts and OEICs carried an annual management charge of, typically, 1.5 per cent.

Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives
by David Sumpter
Published 18 Jun 2018

Virgil’s criticism of Nate’s methods on the basis of Diggler’s success is misplaced. It is simply untrue that Nate’s model is not falsifiable – it can be tested, and I’ll do so over the next pages. In fact, the publication of Virgil’s article in the Washington Post has a heavy component of what psychologists call ‘selection bias’ and what finance guru Nassim Taleb calls being ‘fooled by randomness’. The story only made the paper because Diggler’s predictions worked so well; other pundits (real or fictional) who failed to make accurate predictions were forgotten. While it is entertaining that Diggler made so many correct picks, once they are lifted out of the world of satire they have no validity whatsoever.

After the Gig: How the Sharing Economy Got Hijacked and How to Win It Back
by Juliet Schor , William Attwood-Charles and Mehmet Cansoy
Published 15 Mar 2020

In contrast to some platforms that have quietly stopped making environmental claims, Airbnb has doubled down, producing glossy reports with evidence that its listings are lower-impact than hotel stays.41 This is likely correct, although it’s also the case that there’s limited overlap between Airbnb customers and guests at the big downtown hotels. There’s also likely “selection bias” at work because travelers who opt for Airbnb may already lead more environmentally conscious lifestyles, wherever they stay. But whatever the outcome of that comparison, the bigger question is whether Airbnb leads to more trips because it reduces costs. We found evidence of this “induced travel” effect.

The Authoritarians
by Robert Altemeyer
Published 2 Jan 2007

They are 48.5 years old on the average and went to school for an average of 13.9 years. [The 160 high fundamentalists averaged slightly lower in age (47.7 years) and education (13.7).] But the overall sample probably provides a reasonably good cross-section of the parents whose children attend the large public university in my province. I never have found a self-selection bias for RWA, for example, in these parent studies, and while I worry that some students may fill out the questionnaires themselves, my past inquiries about this in a super-anonymous setting have revealed only about 2% do so. If you think parents of university students are reasonably normal folks, then this is probably a reasonably representative draw of a rather normal population.

Animal: The Autobiography of a Female Body
by Sara Pascoe
Published 18 Apr 2016

It places us somewhere between the complete polyamory of chimpanzees and the definite monogamy of gibbons; human pair bonds are not the end of our mating life, nor are they as sexually exclusive as we might like – which the next chapter will explore. ‘But please,’ you beg me, with a thirst for summary and conclusion, ‘tell us, Sara, what is LOVE?’ It’s so complicated and obviously we all experience it differently, but here you are – for your quote book: ‘Love is a compulsive motivation towards a certain person ruled by evolutionary selection bias and a neurochemical reward system’ (Pascoe 2016). Let me know if you want that printed up on a baseball cap. * I’m worried this reference is too old. You see, Julia Roberts once made a movie called The Runaway Bride. It reunited her with Richard Gere after that successful film they made about how fun and sexy it is to be a prostitute.

The Lost Decade: 2010–2020, and What Lies Ahead for Britain
by Polly Toynbee and David Walker
Published 3 Mar 2020

In Ali Smith’s Autumn, on Brexit day, ‘All across the country there was misery and rejoicing,’ and a house in a village is daubed with ‘Go Home’. Sifting through the mass of books published, plays promoted, songs recorded and programmes and films made for distribution on the internet or in cinemas, patterns can be distinguished, but any content analysis is bound to be rough and ready, running the risk of selection bias, overinterpretation and reductionism. Novels by Sunjeev Sahota, Guy Gunaratne and Kamila Shamsie wove narratives around migration, terrorism and the life and times of people from minority backgrounds; on the stage, Nine Night and Barber Shop Chronicles showed aspects of the migrant experience; Small Island, a new play based on Andrea Levy’s novel, produced at the National Theatre, explored British West Indian relationships, the nature of which had been exposed, again, by the scandalous treatment of the Windrush generation.

The Loop: How Technology Is Creating a World Without Choices and How to Fight Back
by Jacob Ward
Published 25 Jan 2022

And then, when I discovered that the test wasn’t wrong, I was embarrassed! Mortified!” Banaji and her colleagues Tony Greenwald and Brian Nosek put the test online in 1998, hoping that perhaps five hundred people might take it in the first year. Instead, forty-five thousand people took the test in the first month. That popularity raised the question of selection bias. They worried that only liberal people who consciously wanted to fight prejudice would take it, skewing the results. But that quickly faded as people of all backgrounds poured in, day after day. “I remember one day looking at it, and we noticed 400 people from Topeka, Kansas, had taken the test,” Banaji says.

Going Solo: The Extraordinary Rise and Surprising Appeal of Living Alone
by Eric Klinenberg
Published 1 Jan 2012

In fact, one important scholarly article based on a random national sample of 2,248 adults age eighteen and over found: “Contrary to what would be predicted . . . unmarried persons who live alone are in no worse, and on some indicators are in better, mental health than unmarried persons who live with others.” See Michael Hughes and Walter Gove, “Living Alone, Social Integration, and Mental Health,” American Journal of Sociology 87 (1981), no. 1: 48–74. The authors look closely at the question of whether there is a selection bias that determines who lives alone—i.e., are people who live alone different from those who live with others, and are differences that are unrelated to their residential status the things that most affect their mental health. Their conclusion: “The data clearly suggest that in a representative population, a process of social selection related to poor mental health does not appear to play a significant role in determining who will live alone” (p. 62). 14.

Laziness Does Not Exist
by Devon Price
Published 5 Jan 2021

When massively successful stars attribute their good fortune entirely to how diligently they’ve worked, they set people up to have unrealistic expectations about the odds of success, and how wealth is actually meted out in this country. This is especially troublesome when the work habits being promoted are excessive and dangerous. Our media has a selection bias built into it: we rarely get to hear from the people who worked equally hard but failed or lost everything because of it. The musical comedian Bo Burnham (whose career started on YouTube) describes this phenomenon very well: “Don’t take advice from guys like me who’ve gotten very lucky. Taylor Swift telling you to follow your dreams is like a lottery winner saying ‘Liquidize your assets!

Data Action: Using Data for Public Good
by Sarah Williams
Published 14 Sep 2020

There has been much criticism regarding using data scraped from the web or social media sites because the data is skewed to the people who use these platforms. For example, social media sites such as Foursquare, Twitter, and Flickr are more heavily used in urban environments,31 and within these areas the majority of data is recorded in commercial areas,32 which means the data can only be accurately used to describe these places. Self-selection bias can also be manifest in social media because users tend to be young and upwardly mobile; therefore, analysis of this data tells us more about them than it does about a population of retirees on a limited budget.33 Twitter has a more diverse user population, with more African American and Hispanic users compared to Foursquare, whose users are largely young white males.34 A user's impulse to contribute data also can introduce a certain level of bias.

The Internet Trap: How the Digital Economy Builds Monopolies and Undermines Democracy
by Matthew Hindman
Published 24 Sep 2018

By 2017, 77 percent of U.S. adults owned a smartphone, and 51 percent owned a tablet device.22 News is a popular activity for those who own both sets of devices; 45 percent of Americans say they often get news on a mobile device, while an additional 29 percent say that they sometimes do.23 Unfortunately, though, the shift to mobile and tablet news makes the situation of local newspapers even more precarious. Early audience data seemed to show that tablet users produced higher news engagement than readers on other platforms. Much of this effect, Making News Stickier • 143 however, has turned out to be just selection bias. Affluent, tech-savvy, Apple-loving early adopters are heavy news consumers, a group especially likely to rely on news apps. As tablets and smartphones have diffused, and mostly cheaper Android devices have taken over most of the market, the portion of users relying on apps for all or part of their news actually shrunk.24 Instead of being a dramatic departure, then, news consumption on tablets and smartphones mirrors patterns of news on the web.

How Medicine Works and When It Doesn't: Learning Who to Trust to Get and Stay Healthy
by F. Perry Wilson
Published 24 Jan 2023

When I was in your shoes, there was a program that would bring these successful researchers in to talk to us young’uns, theoretically to inspire us. We’d ask Dr. Award Winner how they got where they got and, inevitably, they would discuss some serendipitous finding that opened the door to this really productive new area of discovery. At the time, I worried this was all selection bias. They weren’t going to bring all the failed researchers in to give us inspirational talks. Was enjoying a successful research career just luck? Choose the right thing to study at the right time, and if you don’t, you fail? The truth is, it is a bit about luck. When your first grant gets reviewed, it’s luck whether the reviewer just had a filling lunch, or a fight with their spouse, or read the world’s greatest grant five minutes ago.

Urban Transport Without the Hot Air, Volume 1
by Steve Melia

This was the first of several business surveys, including one conducted by a university for the city council in which a quarter of respondents reported a fall in turnover. All of these surveys were conducted after the event and were explicitly related to the traffic plan. This made them vulnerable to two common research problems: self-selection bias, where those with an axe to grind are more likely to respond; and policy response bias, where people answer in a certain way hoping to influence public policy. To reduce the risk of such biases, researchers usually try to present surveys in a neutral way, so they are not perceived in relation to a policy controversy.

May Contain Lies: How Stories, Statistics, and Studies Exploit Our Biases—And What We Can Do About It
by Alex Edmans
Published 13 May 2024

With regulation, the burden of proof to prosecute someone for misinformation is very high. For best practices, it’s much lower; someone who consistently violates best practices can be censured even if they cannot be prosecuted. ** One book that has commented on the limited signal provided by endorsements is Nassim Taleb’s Fooled by Randomness. Taleb highlights selection bias – publishers reach out to many potential endorsers and only include the most positive ones. Here, we highlight a separate issue – even if publishers included all endorsements, they’d be overly positive as there’s no deterrent to inflation. 1 Lord, Charles G., Mark R. Lepper and Elizabeth Preston (1984): ‘Considering the opposite: a corrective strategy for social judgment’, Journal of Personality and Social Psychology 47, 1231–43. 2 Fong, Geoffrey T., David H.

Darwin's Dangerous Idea: Evolution and the Meanings of Life
by Daniel C. Dennett
Published 15 Jan 1995

Many worlds might have been botched and bungled, throughout an eternity, ere this system was struck out: Much labour lost: Many fruitless trials made: And a slow, but continued improvement carried on during infinite ages of world-making. [Pt. V.] Hume imputes the "continued improvement" to the minimal selective bias of a "stupid mechanic," but we can replace the stupid mechanic with something even stupider without dissipating the lifting power: a purely algorithmic Darwinian process of world-trying. Though Hume obviously didn't think this was anything but an amusing philosophical fantasy, the idea has recently been developed in some detail by the physicist Lee Smolin (1992).

…

The idea that evolution is an algorithmic process is the idea that it must have a useful description in substrate-neutral terms. As George Williams proposed many years ago (1966, p. 25): "In evolutionary theory, a gene could be defined as any hereditary information [emphasis added] for which there is favorable or unfavorable selection bias equal to several or many times its rate of endogenous change." The importance of the separation between information and vehicle is even easier to discern in the case of memes.5 The obvious problem noted by all is that it is very unlikely — but not quite impossible — that there is a uniform "brain language" in which information is stored in different human brains, and this makes brains very different from chromosomes.

Reinventing Discovery: The New Era of Networked Science
by Michael Nielsen
Published 2 Oct 2011

Not long after receiving Dezenhall’s advice, the publishers’ association launched an organization called PRISM, the Partnership for Research Integrity in Science and Medicine. PRISM began a publicity initiative arguing against open access policies such as the NIH policy, claiming that open access would threaten “the economic viability of journals and the independent system of peer review” and potentially introduce “selective bias into the scientific record.” The Dezenhall-PRISM story is just one skirmish of many in the battle between some traditional scientific publishers and the open access movement. On the one hand, we have a situation where open access poses a threat to the profits and ultimately the jobs of both the traditional scientific publishing companies and the not-for-profit scientific societies.

Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets
by Nassim Nicholas Taleb
Published 1 Jan 2001

There is a high probability of the investment coming to you if its success is caused entirely by randomness. This phenomenon is what economists and insurance people call adverse selection. Judging an investment that comes to you requires more stringent standards than judging an investment you seek, owing to such selection bias. For example, by going to a cohort composed of 10,000 managers, I have 2/100 chances of finding a spurious survivor. By staying home and answering my doorbell, the chance of the soliciting party being a spurious survivor is closer to 100%. Reverse Survivors We have so far discussed the spurious survivor—the same logic applies to the skilled person who has the odds markedly stacked in her favor, but who still ends up going to the cemetery.

The Despot's Accomplice: How the West Is Aiding and Abetting the Decline of Democracy
by Brian Klaas
Published 15 Mar 2017

Then, as more and more people dropped landlines in favor of mobile phones, pollsters adapted and tried to ensure that an appropriate percentage of their respondents were mobile phone users.37 These approaches were deemed vastly superior to online polling, which tended to have huge problems with selection bias—primarily because it oversampled young people who made up a disproportionately large share of Internet users but a disproportionately small share of the electorate. That problem is diminishing, slowly but surely, as Internet usage € 174 THE NEW BATTLEGROUND becomes more equally distributed demographically over time.

The Road to Somewhere: The Populist Revolt and the Future of Politics
by David Goodhart
Published 7 Jan 2017

And also for restricting it only to couples with children. Marriage advocates argue that couples are far more likely to stay together if married rather than just cohabiting—and it is true that one in eleven married couples separate before their child’s fifth birthday compared with one in three unmarried couples. But there is a selection bias here: high-commitment people choose marriage and low-commitment people choose cohabitation. Nevertheless, greater financial benefit and social recognition might nudge more wavering cohabiters to take the leap into marriage, which is still somewhat harder to undo than cohabitation. Fully transferable tax allowances between couples would be costly, about £5 billion a year (depending on who was entitled).

Five Billion Years of Solitude: The Search for Life Among the Stars
by Lee Billings
Published 2 Oct 2013

This is something that I think at most only ten percent of surveyed stars can now support. At least as far as Jupiter is concerned, our solar system is somewhat unusual. Right now, the data are telling us that the archetypal planetary system is a Neptune-like planet in a warm, short-period orbit, but part of that is selection bias—those planets are easier to detect.” He began to deliberately pace the floor, regaining the rhythm of his presentation by walking back and forth between the podium and a window. “We really don’t know much yet about the distribution of Earth-size planets in Earth-like orbits, but the expectation is that they will be abundant.

The Rise and Decline of Nations: Economic Growth, Stagflation, and Social Rigidities
by Mancur Olson

Whewell argued that "no example can be pointed out, in the whole history of science, so far as I am aware, in which this consilience . . . has given testimony in favor of an hypothesis later discovered to be false. "4 There is also a need to ensure that an explanation is not consistent with the evidence presented in support of it merely because those cases that happened to fit the theory were the only ones examined; there could, of course, be other cases to which the theory is supposed to apply that contradict it. Since it is usually impossible in practice to consider all relevant cases, the best approach is often to consider all the cases in one pre-established category or another; this rules out selection bias at least in this category. Possibly different principles apply to the category considered than to other categories, and this suggests examining other cases outside the test category as well. If the facts are not selected because they fit a theory, and they are also numerous and in very different classes, then it is most improbable that a false theory could explain them and at the same time remain parsimonious.

The Sum of Small Things: A Theory of the Aspirational Class
by Elizabeth Currid-Halkett
Published 14 May 2017

NANNIES, HOUSEKEEPERS, AND THE PRICE OF TIME Similarly, child care in the form of nannies over daycare is an urban luxury and reality especially in Washington, DC, and the Connecticut and New Jersey suburbs.38 Without question, nannies cost more in cities, but people in cities tend to work more, too, and those labor market elites tend to have the money to afford nannies versus daycare. Again, in the case of New York, there is a self-selection bias: Those who can afford nannies tend to make enough money that the expenditure makes only a tiny dent in overall expenditures. Housekeeping services are also a remarkably urban phenomenon, with New York, Los Angeles, and San Francisco spending almost double the rest of the country (whereas housekeeping supplies is a disproportionately non-metro purchase).

Risk Management in Trading
by Davis Edwards
Published 10 Jul 2014

It does not take long to identify the combination of investments that would have maximized returns over a historical period and develop some rule that would have led to those investments. However, those rules are seldom useful for identifying opportunities in the future. Historical testing also has a selection bias because of the strong relationship between risk and return. The best trading strategies typically demonstrate consistent good performance with very low risk. In other words, successful trading strategies have a better risk/return relationship than other investments. However, because these strategies aren’t taking on the most risk, they are seldom the most profitable strategies.

The Alternative: How to Build a Just Economy
by Nick Romeo
Published 15 Jan 2024

Douglas Holtz-Eakin, “Conservative Economist Blames High Unemployment on ‘Richer’ Benefits,” interview by Michel Martin, All Things Considered, NPR, May 16, 2021, https://www.npr.org/2021/05/16/997339431/conservative-economist-blames-high-unemployment-on-richer-benefits. 16. Richard K. Vedder, “The Transformation of Economics,” Wall Street Journal, March 1, 2016, http://www.wsj.com/articles/the-transformation-of-economics-1456875011. 17. Hristos Doucouliagos and T. D. Stanley, “Publication Selection Bias in Minimum-Wage Research? A Meta-Regression Analysis,” British Journal of Industrial Relations 47, no. 2 (June 2009): 406–428, https://doi.org/10.1111/j.1467-8543.2009.00723.x. 18. The Initiative on Global Markets, “Minimum Wage,” February 26, 2013, https://www.igmchicago.org/surveys/minimum-wage/. 19.

Good Calories, Bad Calories: Challenging the Conventional Wisdom on Diet, Weight Control, and Disease
by Gary Taubes
Published 25 Sep 2007

This is why the practice of science requires an exquisite balance between a fierce ambition to discover the truth and a ruthless skepticism toward your own work. This, too, is the ideal albeit not the reality, of research in medicine and public health. In 1957, Keys insisted that “each new research adds detail, reduces areas of uncertainty, and, so far, provides further reason to believe” his hypothesis. This is known technically as selection bias or confirmation bias; it would be applied often in the dietary-fat controversy. The fact, for instance, that Japanese men who lived in Japan had low blood-cholesterol levels and low levels of heart disease was taken as a confirmation of Keys’s hypothesis, as was the fact that Japanese men in California had higher cholesterol levels and higher rates of heart disease.

…

By the 1970s, it was as if the two sides had lived through two entirely different decades of research. They could not agree on the dietary-fat hypothesis; they could barely discuss it, as Henry Blackburn had noted, because they were seeing two dramatically different bodies of evidence. Another revealing example of selection bias was the reanalysis of a study begun in 1957 on fifty-four hundred male employees of the Western Electric Company. The original investigators, led by the Chicago cardiologist Oglesby Paul, had given them extensive physical exams and come to what they called a “reasonable approximation of the truth” of what and how much each of these men ate.

Bad Science
by Ben Goldacre
Published 1 Jan 2008

Let’s say 70 per cent of all women want Prince Charles to be told to stop interfering in public life. Oh, hang on—70 per cent of all women who visit my website want Prince Charles to be told to stop interfering in public life. You can see where we’re going. And of course, in surveys, if they are voluntary, there is something called selection bias: only the people who can be bothered to fill out the survey form will actually have a vote registered. There was an excellent example of this in the Telegraph in the last days of 2007. ‘Doctors Say No to Abortions in their Surgeries’ was the headline. ‘Family doctors are threatening a revolt against government plans to allow them to perform abortions in their surgeries, the Daily Telegraph can disclose.’

Mindware: Tools for Smart Thinking
by Richard E. Nisbett
Published 17 Aug 2015

The long-term unemployed may have poor employment records, or be lackadaisical in job hunting, or be too picky about the kind of job they would do. Politicians routinely invoked these alleged causes during the Great Recession. But you can’t know whether these explanations are correct by conducting a multiple regression analysis. No amount of “controlling” for such variables will get rid of self-selection bias and tell you whether there is hiring prejudice. The only way to answer the question is with an experiment. And the experiment has been done; we know the answer. The economists Rand Ghayad and William Dickens sent out 4,800 fictitious applications for six hundred job openings.14 Even when applications were identical except for alleged length of unemployment, the short-term unemployed were twice as likely to get an interview as the long-term unemployed.

The Idealist: Aaron Swartz and the Rise of Free Culture on the Internet
by Justin Peters
Published 11 Feb 2013

33 the learned gentleman asked in frustration after a particularly pernicious attack on his probity. There was no conspiracy—or, at least, none of the tinfoil-hat variety. The law’s framers’ narrow conception of what constituted the public’s best interest was less a function of malice or sedition than of selection bias. The bill had been conceived and written by “the most representative organizations that we could think of or that were brought to our attention as having practical concern in the amelioration of the law, but especially, of course, those concerned in an affirmative way—that is to say, in the protection of the right,” Putnam acknowledged at the outset of the hearings.34 One man’s right is another’s wrong.

The Elusive Quest for Growth: Economists' Adventures and Misadventures in the Tropics
by William R. Easterly
Published 1 Aug 2002

Economists looked mainly at those that were winners at the end, because those were the countries thathad the good-quality data. (Also, economists Solow’s Surprise 65 from rich countries prefer to talk aboutand visit other rich countries.) The winners write economic history. EvenMaddison’s sample suffered alot from the selection bias toward winners, as it includes only eight countries that the World Bank today classifies as poor-less than a third of the sample. Since poor nations makeup the vast majority of all countries in the world, this is still a severe bias in favor of those that have wound up rich today. The Maddison sample whose1820 income can be guessedhas no country from Africa, for example.

This Will Make You Smarter: 150 New Scientific Concepts to Improve Your Thinking
by John Brockman
Published 14 Feb 2012

We live in the world, however, not in a laboratory, and bias cannot be eliminated. Bias, critically utilized, sharpens the collection of data by knowing when to look, where to look, and how to look. It is fundamental to both inductive and deductive reasoning. Darwin didn’t collect his data randomly or disinterestedly to formulate the theory of evolution by natural selection. Bias is the nose for the story. Truth needs continually to be validated against all evidence that challenges it fairly and honestly. Science, with its formal methodology of experimentation and the reproducibility of its findings, is available to anyone who plays by its rules. No ideology, religion, culture, or civilization is awarded special privileges or rights.

Top Dog: The Science of Winning and Losing
by Po Bronson and Ashley Merryman
Published 19 Feb 2013

A Tale of Identity Conflict,” http://d.doiorg/102139/ssrn1907727 (2012) Cárdenas, Juan-Camilo, Anna Dreber, Emma von Essen, & Eva Ranehill, “Gender Differences in Competitiveness and Risk Taking: Comparing Children in Colombia and Sweden,” Journal of Economic Behavior & Organization, vol. 83(1), pp. 11–23 (2012) Casari, Marco, John C. Ham, & John H. Kagel, “Selection Bias, Demographic Effects, and Ability Effects in Common Value Auction Experiments,” American Economic Review, vol. 97(4), pp. 1278–1304 (2007) Cotton, Christopher, Frank McIntyre, & Joseph Price, “Gender Differences in Competition: A Theoretical Assessment of the Evidence,” The Selected Works of Christopher Cotton, http://bit.ly/Q654OM (2011) Dargnies, Marie-Pierre, “Men Too Sometimes Shy Away from Competition: The Case of Team Competition,” http://d.doiorg/102139/ssrn1814989 (2011) Dreber, Anna, Interview with Author (2011) Dreber, Anna, Christer Gerdes, & Patrik Gränsmark, “Beauty Queens and Battling Knights: Risk Taking and Attractiveness in Chess,” IZA Discussion Paper No. 5314, Institute for the Study of Labor (2010) Dreber, Anna, Emma von Essen, & Eva Ranehill, “Outrunning the Gender Gap—Boys and Girls Compete Equally,” Experimental Economics, vol. 14(4), pp. 567–582 (2011) Eckel, Catherine C., & Philip J.

The Crux
by Richard Rumelt
Published 27 Apr 2022

He noted, “Engineering schools gradually became schools of physics and mathematics; medical schools became schools of biological science; business schools became schools of finite mathematics.”4 My own life experience supports Simon’s comment about the replacement of design with deduction in professional schools. For the academics who currently populate top professional schools, design is a bit like shop class, akin to automobile repair or welding, and residing at a far remove from respectable activities like the mathematical modeling of stochastic processes and the statistical analysis of selection bias. Study marketing in most masters in business administration (MBA) programs and you will be exposed to theory about consumer behavior and the concept of market segments, but will have little insight into the wide variety of actual company marketing programs. The students will find that they cannot deduce a real-world marketing program from the theory of consumer behavior.

Autistic Community and the Neurodiversity Movement: Stories From the Frontline
by Steven K. Kapp
Published 19 Nov 2019

Bascom (Ed.), Loud hands: Autistic people, speaking (pp. 315–319). Washington, DC: The Autistic Press. 29. Carson, D. (2017). Walking with Joaquin. TEDx Talks (Video file). Retrieved from https://www.youtube.com/watch?v=ruXB3lbiD3U. 30. Russell, G., Mandy, W., Elliott, D., White, R., Pittwood, T., & Ford, T. (2019). Selection bias on intellectual ability in autism research: A crosssectional review and meta-analysis. Molecular Autism, 10 (1), 9. 31. Stedman, A., Taylor, B., Erard, M., Peura, C., & Siegel, M. (2018). Are children severely affected by autism spectrum disorder underrepresented in 22 Conclusion 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 317 treatment studies?

Exceptional People: How Migration Shaped Our World and Will Define Our Future
by Ian Goldin , Geoffrey Cameron and Meera Balarajan
Published 20 Dec 2010

Incidents of infant mortality, breast and cervical cancer, sexually transmitted infections, heart disease, diabetes, teen pregnancy, suicide, tobacco use, and alcohol use were all found to be generally lower among immigrants than native-born U.S. citizens.174 A study by the Canadian government finds that recent immigrants, particularly from non-European countries, are in better health than their Canadian-born counterparts.175 These effects are achieved through a combination of selection bias (people healthier than the norm are more likely to migrate) and recent migrants taking advantage of the higher incomes and health facilities that accompany moving to a more developed country. The longer migrants stay in destination countries, however, the more their “health advantage” appears to dissipate.

A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation
by Richard Bookstaber
Published 5 Apr 2007

The person on the other side of the trade might have insider information on the company, or he might know that there is a much larger overhang of potential selling, the demand the buyer sees being a first trickle in what will emerge as a flood of selling. Even beyond the problem of adverse selection, if somebody waves a white flag and tries to overcome the adverse selection bias by announcing who they are—confirming to everyone’s satisfaction that they are trading strictly because of a liquidity need and have no special information or view of the market and are willing to discount the price an extra point to get someone to take the position off their hands—the trader who buys the position still faces a risk because there is no guarantee the price will not fall further between the time the trader takes on the position and the time he seeks to resell it.

Culture & Empire: Digital Revolution
by Pieter Hintjens
Published 11 Mar 2013

All this makes it possible for interesting strangers to walk up and look at your work and, if they like it and feel challenged by it, get involved little by little. You want to be working on your seed in public view, and talking about your new project, from the very start. This means people can make suggestions, and feel involved, from day one. If we, as founders of a group, choose those we work with, we're building in "selection bias." It is much easier to work with those nice, smart people who agree with us, than the idiots and critics who disagree. And when you agree with me, you just confirm all of my biases and assumptions and I know from experience that those can be wrong in the most amazing ways. Over time, collecting people who share the same broken assumptions and biases can kill a project.

The Case Against Sugar
by Gary Taubes
Published 27 Dec 2016

* * * *1 Much of the content in this chapter about the Sugar Association and its defense of sugar was first published as an article in the November–December 2012 issue of Mother Jones, which I co-authored with Cristin Kearns. Cristin unearthed all the sugar industry documents on which the article and this chapter rely. *2 This study was completed in 1973 but not officially published until 1989, because, as the lead investigator told me, “We never saw the results that we thought we would.” This kind of selection bias was all too common in this research. *3 This same comparison would be made by Campbell and others between the disease spectrum in black Africans and in blacks in the United States, who had been (forcibly) removed from Africa only a few hundred years earlier. The comparison strongly implied that something other than genetics was involved in these chronic diseases; some aspect of diet or lifestyle had to be triggering the disease that was present in the United States and relatively absent in Africa

Fed Up: An Insider's Take on Why the Federal Reserve Is Bad for America
by Danielle Dimartino Booth
Published 14 Feb 2017

Though compulsively profane, flinging obscenities at the drop of a hat, Geithner was gracious and professional with colleagues. Rubin described Geithner as “elbow-less,” with a calm and easy manner. Geithner later admitted that his previous jobs “mostly exposed me to talented senior bankers and selection bias probably gave me an impression that the U.S. financial sector was more capable and ethical than it really was.” An understatement if there ever was one. A lawyer at the New York Fed, who at times played basketball with Geithner on the Fed’s fourteenth-floor court, described his boss as a fantastic player with a notable lack of nerve when it came to making the big shot.

Doing Data Science: Straight Talk From the Frontline
by Cathy O'Neil and Rachel Schutt
Published 8 Oct 2013

Some of their methods were small and qualitative, some of them larger and quantitative. Given a random sample of 100,000 users, they set out to determine the popular names and categories of names given to circles. They identified 168 active users who filled out surveys and they had longer interviews with 12. The depth of these interviews was weighed against selection bias inherent in finding people that are willing to be interviewed. They found that the majority were engaging in selective sharing, that most people used circles, and that the circle names were most often work-related or school-related, and that they had elements of a strong-link (“epic bros”) or a weak-link (“acquaintances from PTA”).

Miracle Cure
by William Rosen
Published 14 Apr 2017

Fifty-five of them were assigned to the treatment group, which would be given streptomycin and bed rest, while the fifty-two members of the control group would receive bed rest and a placebo: a neutral pill or injection indistinguishable to the patient from the compound being tested. The two groups were selected entirely at random, based on numbers chosen blindly by the investigator and placed in sealed envelopes, thus assuring that no selection bias would occur unconsciously. Nor were the patients themselves told whether they were receiving streptomycin or a placebo. Hill insisted that results of the test wouldn’t depend on clinical evaluation alone, but on changes in chest X-rays, which would be examined by radiologists unaware of whether the subject had been in the treatment or the control group.

Everyday Utopia: What 2,000 Years of Wild Experiments Can Teach Us About the Good Life
by Kristen R. Ghodsee
Published 16 May 2023

One 2004 study revealed that while people living in communities with shared treasuries had less economic capital, their lack of material resources was more than compensated for by what the authors called “social,” “human,” and “natural” capital, meaning that community residents benefited from healthier and more stable social relations, more opportunities to develop their unique skills and talents, and more time spent in nature.55 A 2018 study of 913 residents in intentional communities in the United States and Canada found that residents reported levels of personal well-being that were among the highest ever recorded in a multinational comparison group that included thirty-one other representative studies.56 Although you have to consider the selection bias inherent in surveying people who have chosen to live in intentional communities, the authors of the study believed that their findings supported “the contention that sustainability, in the form of a communal lifestyle of a low ecological footprint, may be promoted without forfeiting well-being.”57 As in cohousing communities, it may also be that women particularly benefit from living in intentional communities, where their domestic labors are valued as work and where shared chores create time savings through economies of scale.

Common Knowledge?: An Ethnography of Wikipedia
by Dariusz Jemielniak
Published 13 May 2014

Other motivations may be ideological (e.g., a strong belief that information should be free) and driven by principle (Nov, 2007; E. G. Coleman, 2013). Incidentally, even as beginners, those who become die-hard Wikipedians differ in how they participate in the community and edit (Panciera, Halfaker, & Terveen, 2009). This could indicate a significant selection bias in the Wikipedia community. Long-standing Wikipedians are sometimes perceived as a different kind of person by newcomers, and this perception adversely affects retention of new editors (Antin, 2011). They also take the role of gatekeepers in the eyes of the newcomers, especially in the case of breaking news articles (Keegan & Gergle, 2010).

I Think You'll Find It's a Bit More Complicated Than That
by Ben Goldacre
Published 22 Oct 2014

said Time magazine, half a century ago. That figure sounded pretty high: Huff chases it, and points out the flaws. How did they find all these people they asked? Who did they miss? Losers tend to drop off the alma mater radar, whereas successful people are in Who’s Who and the College Record. Did this introduce ‘selection bias’ into the sample? And how did they pose the question? Can that really be salary rather than investment income? Can you trust people when they self-declare their income? Is the figure spuriously precise? And so on. In the intervening fifty years this book has sold one and a half million copies.

When Einstein Walked With Gödel: Excursions to the Edge of Thought
by Jim Holt
Published 14 May 2018

Interestingly, Dorothy Day, the heroine of the Catholic Worker Movement, has so far escaped this sort of revisionist soul blackening. Evidently, her saintliness was balanced by piquant elements left over from her bohemian-libertine past. She had redeeming vices. Still, looking only at world-famous do-gooders leaves one open to the charge of selection bias. What happens when a fairly ordinary person takes up the cause of goodness with uncompromising zeal? That question is Nick Hornby’s point of departure in his 2001 comic novel, How to Be Good. The novel’s narrator is Katie Carr, a doctor in her forties who works at a depressing clinic in North London.

The Missing Billionaires: A Guide to Better Financial Decisions
by Victor Haghani and James White
Published 27 Aug 2023

Results can take the form of corner solutions, which are extreme and unrealistic. Fortunately, there has been a lot of progress in developing better‐behaved optimization techniques that can mitigate these issues. The optimal portfolios suggested by these enhanced optimizers tend to be less susceptible to negative selection bias and parameter estimation errors. As we've discussed already, Expected Utility is usually quite flat in the vicinity of optimality, which means a small sacrifice in Risk‐adjusted Return can give us a portfolio that is more balanced and robust than the unconstrained, theoretically optimal portfolio.

Rationality: What It Is, Why It Seems Scarce, Why It Matters
by Steven Pinker
Published 14 Oct 2021

But Head Start would be ill advised to prepare children for school by fitting them with larger sneakers. Just as dangerous is the collider, where unrelated causes converge on a single effect. Actually, it’s even more dangerous, because while most people intuitively get the fallacy of a confound (it cracked them up in the shtetl), the “collider stratification selection bias” is almost unknown. The trap in a causal collider is that when you focus on a restricted range of effects, you introduce an artificial negative correlation between the causes, since one cause will compensate for the other. Many veterans of the dating scene wonder why good-looking men are jerks.

The Invisible Hands: Top Hedge Fund Traders on Bubbles, Crashes, and Real Money
by Steven Drobny
Published 18 Mar 2010

Very talented managers and risk takers will typically want to run their own business their own way, or work in a small group with like-minded partners. They do not want to work for big, bureaucratic institutions, even if they are able to start with much larger amounts of capital. Because of this, institutions have a huge negative selection bias on hiring and trying to build out hedge fund platforms. Further, if you are successful, you will have the Harvard compensation problem of portfolio managers making multiples of what the president makes. What is your outlook for the hedge fund business over the next 5 to 10 years? Although we are in the midst of a necessary shakeout in the broader hedge fund industry, the returns for hedge funds as a whole were better than most other investing categories during 2008.

Producing Open Source Software: How to Run a Successful Free Software Project
by Karl Fogel
Published 13 Oct 2005

This will not be good for either the project or the organization, in the long run. * * * [50] By the way, those common components are quite often open source libraries themselves. These days, it's typical for a proprietary software product to contain a lot of open source code, with a layer of proprietary custom code wrapped around the outside. [51] While some selection bias no doubt informs my experience — after all, the consultant tends to get brought in when things are going wrong, not when they're going right — my assertion that proprietary vendors don't get open source right if left to their own habits is based not just on my own experiences but also on talking to many other people, who report the same finding with remarkable consistency

The Book of Woe: The DSM and the Unmaking of Psychiatry
by Gary Greenberg
Published 1 May 2013

He didn’t point out the lunacy of spending all that time (including mine) and money to find out not whether the criteria or the cross-cutting measures were reliable or valid, but rather only whether clinicians liked the DSM-5, as if the APA were looking for Facebook friends. He didn’t raise the question of selection bias, that is, whether or not the same factors that motivated the few volunteers who actually followed through also predisposed them to give the DSM a Like. He didn’t have to do any of this. Nor did he have to deconstruct propaganda or slog through weedy statistics. He just did the simple math and came to the obvious conclusion.

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined
by Lasse Heje Pedersen
Published 12 Apr 2015

and (3) how does variation in the predictor translate into future predicted returns? Each of these steps is difficult to do in real time, and backtests are often subject to biases with respect to some or all of them. First, choosing the predictor is not easy, and backtests often tend to look at variables that have worked in the past, a selection bias as they may not work in the future. Second, knowing whether a predictor is high or low is based on limited historical evidence or guidance from judgment and economic theory. This is easy, however, in an in-sample (i.e., cheating) backtest, but in 1932 investors did not know that this dividend was at an all-time high compared to the next 80 years, and in 2000 investors did not know that the dividend yield would not get any lower in the next decade (even if judgment might have suggested that these values were extreme).

Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth
by Stuart Ritchie
Published 20 Jul 2020

This is because IQ and conscientiousness both increase the chances of attending college in the first place, selecting out people who are low on both those traits. The fact that low-IQ, low-conscientiousness people are missing from the college sample produces a spurious correlation between those two variables. It’s a wicked problem, and more pervasive in studies than we’d like to think. See also Marcus R. Munafò et al., ‘Collider Scope: When Selection Bias Can Substantially Influence Observed Associations’, International Journal of Epidemiology 47, no. 1 (27 Sept. 2017): pp. 226–35; https://doi.org/10.1093/ije/dyx206 25.  If you really want to freak yourself out, read about the philosopher David Hume’s ‘Problem of Induction’, which essentially states that correlation isn’t even correlation – there’s no rational basis for arguing that things that have happened before will happen again.

Wall Street: How It Works And for Whom
by Doug Henwood
Published 30 Aug 1998

The Golden Age Is In Us (New York and London: Verso). Cohen, Darrel, Kevin Hassett, and Jim Kennedy (1995). "Are U.S. Investment and Capital Stocks at Optimal Levels?," Federal Reserve Board, Finance and Economics Discussion Series No. 95-32 Quly)- Cohen, Randolph B., and Christopher K. Polk (1996). "COMPUSTAT Selection Bias in Tests of the Sharpe-Lintner-Black CAPM," unpublished paper. University of Chicago, Graduate School of Business (January). Collins, Joseph, and John Lear (1995). Chile's Free Market Miracle: A Second Look (San Francisco: Food First Books). Community Investment Monitor (1995). "Member Profile: Institute for Community Economics Revolving Loan Fund," Community Investment Monitor (yf/inter), p. 3- Corbett, Jenny, andTimJenkinson (1993)- "The Financing of Industry, 1970-89: An International Comparison," mimeo, Oxford University.

The Content Trap: A Strategist's Guide to Digital Change
by Bharat Anand
Published 17 Oct 2016

We only talk about the large and successful projects like Wikipedia, Linux, or Apache. But the vast majority of these projects never mobilize anybody.” Studying successful open-source projects is useful. But to understand what makes successful projects really work, you’ve got to study failed ones too. “It’s the familiar problem of selection bias,” Mako Hill noted. It’s the reason why studies of successful CEOs can’t offer much guidance: The characteristics observed—for example, that successful leaders are inspiring, or data driven—may also be common among CEOs who failed but weren’t studied. So why did Wikipedia work where similar projects failed?

Energy: A Human History
by Richard Rhodes
Published 28 May 2018

None died of leukemia, a classic disease of serious radiation overexposure. Cancer deaths were below comparable averages among the general population. In the dubious tradition of radiation-dose science, the outcome study assigned this reduced mortality following low-level radiation exposure to a healthy-worker effect. That effect, a form of selection bias, results from workers being healthier on average than the general population—healthy enough to work. In the Chalk River study, however, deaths from lung cancer (probably from smoking) and from cardiovascular disease were higher among the exposed than among the general Ontario population. If the healthy-worker effect applied, then the radiation workers’ reduced mortality was all the more remarkable.26 Fear of radiation and misunderstanding of its effects were powerful drivers of antinuclear sentiment.

Doing Harm: The Truth About How Bad Medicine and Lazy Science Leave Women Dismissed, Misdiagnosed, and Sick
by Maya Dusenbery
Published 6 Mar 2018

Most had initially been given a wrong diagnosis or gotten some version of “it’s all in your head”: 15 percent were told they had IBS, 13 percent said that their doctors just told them “nothing was wrong,” 12 percent got a label of “stress,” and 6 percent were diagnosed with depression. Thirty percent had actually been given a prescription medication for another condition. Only 20 percent were told that they might have ovarian carcinoma. There were limitations with that first study, including possible selection bias since the survey was sent out through a survivor newsletter, but it was a start. Goff and others went on to do more research on the topic and eventually developed a symptom index. In 2007, the American Cancer Society, the Gynecologic Cancer Foundation, and the Society of Gynecologic Oncologists officially retired the “silent killer” moniker.

Triumph of the Optimists: 101 Years of Global Investment Returns
by Elroy Dimson , Paul Marsh and Mike Staunton
Published 3 Feb 2002

Journal of Finances 32: 261–276 Mitchell, B.R., 1998, International Historical Statistics: Europe 1750–1993. London: Macmillan Press Modigliani, F., and M.H. Miller, 1958, The cost of capital, corporation finance and the theory of investment. American Economic Review 48: 261–297 Moller, B., 1962, The Swedish Stock Market. Gothenburg: AB Seelig & Co Nagel, S., 2001, Accounting information free of selection bias: a new database for the UK 1955–2000, Working paper. London Business School Nelson, C.R., and G.W. Schwert, 1977, Short-term interest rates as predictors of inflation: On testing the hypothesis that the real rate of interest is constant. American Economic Review 67: 478–86 Nielsen, S., and O.

In Pursuit of the Perfect Portfolio: The Stories, Voices, and Key Insights of the Pioneers Who Shaped the Way We Invest
by Andrew W. Lo and Stephen R. Foerster
Published 16 Aug 2021

If bubbles existed, then there should be evidence of predictable price declines. Thus, any prescriptions and policies about reacting to bubbles are based on beliefs rather than reliable empirical evidence. The keyword here is “reliable.” Fama emphasizes the notion of reliability in empirical evidence. He points to the existence of “ex post selection bias.” After a major stock market decline, attention is naturally focused on the few who happen to have predicted such a decline. But to conclude that these forecasters reliably predicted the decline, we would need to consider the forecasters’ complete track records, including all of their incorrect past predictions, as well as the records of other forecasters we might have relied on instead of them.

Spies, Lies, and Algorithms: The History and Future of American Intelligence
by Amy B. Zegart
Published 6 Nov 2021

The Efficacy of Last Resorts In public policy classes, I teach students that good decisionmaking is rational: policymakers should systematically assess the costs and benefits of their options and pick the one that yields the best overall result. Sounds straightforward. It isn’t. For starters, covert action often fails because it’s used only for the toughest foreign policy challenges—the ones that keep presidents awake at night, those in which the stakes are high and the odds of success are inherently low. Academics call this a selection bias problem. It’s one thing to judge a failure rate when your sample is representative, but quite another when the sample is skewed. Imagine judging Michael Jordan’s basketball record only by how many half-court shots he made. Retrieving a sunken Soviet nuclear sub and its hidden secrets,103 arming foreign fighters against Soviet soldiers, and rescuing American hostages are the half-court shots of foreign policy.

Why Stock Markets Crash: Critical Events in Complex Financial Systems
by Didier Sornette
Published 18 Nov 2002

The different models will correspond to different implementations of the theory of critical points with log-periodic power laws. Different scenarios will be generated for each model by the different solutions obtained by the fitting procedure. HOW TO DEVELOP AND INTERPRET STATISTICAL TESTS OF LOG-PERIODICITY Before studying the issue of prediction, the question of a possible selection bias of the fitted financial time series presented in chapters 7 and 8 must be addressed. By selecting time windows on the basis of the existence of (1) a change of regime and acceleration of the market price and of (2) a crash or large correction at their end, we may have pruned the data so that, by chance alone, the fits with the log-periodic power law formula may have been qualified.

Not Working: Where Have All the Good Jobs Gone?
by David G. Blanchflower
Published 12 Apr 2021

Washington, DC. Irwin, D. A. 2017. “The False Promise of Protectionism: Why Trump’s Trade Policy Could Backfire.” Foreign Affairs 96 (3): 45–56. Iverson, L., and S. Sabroe. 1988. “Participation in a Follow-up Study of Health among Unemployed and Employed People after a Company Closedown: Drop Outs and Selection Bias.” Journal of Epidemiology and Community Health 42: 396–401. Jackson, P., and P. Warr. 1987. “Mental Health of Unemployed Men in Different Parts of England and Wales.” British Medical Journal 295: 525. Jae, S., D. J. Price, F. Guvenen, and N. Bloom. 2015. “Firming Up Inequality.” NBER Working Paper #21199.

In the Plex: How Google Thinks, Works, and Shapes Our Lives
by Steven Levy
Published 12 Apr 2011

While some commentators wrung hands over the Spock-like nature of the senator’s personality, Googlers swooned over the dispassionate, reason-based approach he took to problem solving. Google employees, through the company PAC, contributed more than $800,000 to his campaign, trailing only Goldman Sachs and Microsoft in total contributions. “It’s a selection bias,” says Eric Schmidt of the unofficial choice of most of his employees. “The people here all have been selected very carefully, so obviously there’s going to be some prejudice in favor of a set of characteristics—highly educated, analytic, thoughtful, communicates well.” Sitting among the Googlers packed in Charlie’s Café on November 14 was one of the company’s brightest young product managers, Dan Siroker.

More Guns, Less Crime: Understanding Crime and Gun-Control Laws
by John R. Lott
Published 15 May 2010

T H E P O L I T I C A L A N D A C A D E M I C D E B AT E BY 1 9 9 8 | 159 The frequencies of missing data are 46.6% for homicide, 30.5% for rape, 12.2% for aggravated assault, and 29.5% for robbery. Thus, the [Lott and Mustard] model excludes observations based on the realization of the dependent variable, potentially creating a substantial selection bias. Our strategy for finessing the missing data problem is to analyze only counties maintaining populations of at least 100,000 during the period 1977 to 1992. . . . Compared to the sample [comprising] all counties, the missing data rate in the large-county sample is low: 3.82% for homicide, 1.08% for rape, 1.18% for assault, and 1.09% for robberies.

Stress Test: Reflections on Financial Crises
by Timothy F. Geithner
Published 11 May 2014

I learned how vulnerable markets could be to herd behavior, to uninformed, indiscriminate shifts in sentiments. Many financiers who lent money in Asia did not seem to know much about the risks they were taking or the countries they were playing in. But I did not view Wall Street as a cabal of idiots or crooks. My jobs mostly exposed me to talented senior bankers, and selection bias probably gave me an impression that the U.S. financial sector was more capable and ethical than it really was. I spent more time with smart executives such as Deryck Maughan, a Salomon Brothers banker I knew in Japan who later became CEO, and smart investors such as Stan Druckenmiller, a hedge fund billionaire I sometimes consulted about markets, than I spent with the sordid elements of the financial industry.

In Pursuit of Privilege: A History of New York City's Upper Class and the Making of a Metropolis
by Clifton Hood
Published 1 Nov 2016

The vital statistics of the full population were entered into a SPSS-based computer program that explored seventy-five variables related to sociological factors such as personal background, education, occupation, place of residence, business affiliations, social associations, and recreational activities. It should be noted that the results of this statistical analysis are not advanced with the view, once commonplace in historical scholarship, that quantification is an infallible method free of the selection bias of qualitative evidence; rather, my belief is that all historical evidence has its uses and its strengths and weaknesses and should be scrutinized and used in tandem with other evidentiary sources. The findings of an interrogation of this data are consistent with other evidence and confirm that this was indeed a privileged group.

Basic Income: A Radical Proposal for a Free Society and a Sane Economy
by Philippe van Parijs and Yannick Vanderborght
Published 20 Mar 2017

Fund i ng, E xper i men ts, and Trans itions time), while �those above sixty kept receiving their state pension of over 500 Namibian dollars. The bulk of the funding came from the German United Evangelical Mission.9 As all residents in the relevant age group received the payment, there � was no individual self-�selection bias and the proj�ect made it pos�si�ble to observe a basic-�income scheme operating at the level of a �whole community. A more carefully designed experiment was conducted in the Indian state of Madhya Pradesh from June 2011 to November 2012 with funding from UNICEF. Essentially, each adult resident in eight randomly chosen villages was entitled to an unconditional basic income of 200 rupees per month (slightly more than $4, or 6.5 �percent of GDP per capita in Madhya Pradesh and 4 �percent of GDP per capita in India at the time).

Buffett
by Roger Lowenstein
Published 24 Jul 2013

.… In such circumstances people are perfectly willing to make up answers, or even to pay others to make them up for then.32 Security analysts, then, were the spiritual descendants of “the medicine man, mystics, astrologers, gurus.” As opposed to these pagan stockpickers, Jensen claimed for “science” the Efficient Market Theory. Noting that the “star pupils of Graham and Dodd” were in attendance, Jensen still claimed that it was hard to tell if “any” were really superior, due to the well-known “selection-bias problem.” If I survey a field of untalented analysts all of whom are doing nothing but flipping coins, I expect to see some who have tossed two heads in a row and even some who have tossed ten heads in a row. Buffett could not have asked for a better setup. The failure of most money managers to do better than coin-flippers had been invoked at every turn.

Food Allergy: Adverse Reactions to Foods and Food Additives
by Dean D. Metcalfe
Published 15 Dec 2008

In three retrospective series of infants with severe persistent distress, abnormally frequent acid reflux was demonstrated by esophageal 24-hour pH monitoring in 15–25% of infants studied [18,22,86]. This exceeds the expected prevalence of 5–10% in young infants [87] and may in part be explained by selection bias in infants referred for gastroenterological investigation. Abnormally frequent or prolonged GER on pH monitoring usually presented with overt regurgitation and non-regurgitant “silent” GER was uncommon [18,22]. The duration of crying and fussing per day did not correlate with the severity of GER [18].

…

For example, different terms are used to describe the “eczematous” condition. This results not only in disease misclassification, but may describe different immunological conditions. Indeed, the role of atopic sensitization in childhood eczema remains obscure as it is neither a prerequisite nor a uniform cause of the disease. Nutritional studies are prone to selection bias and reverse causality. Such bias may arise when atopic families – if aware of public health recommendations – are increasingly motivated to alter dietary practices, either in their own diet or in the diet of their infants. The effects of reverse causality are highlighted in various studies and for different allergic outcomes.

Tools of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers
by Timothy Ferriss
Published 6 Dec 2016

It turns out that when you look at the mortality tables, there’s an 80% chance you’re going to die from cardiovascular disease, cerebrovascular disease, cancer, or neurodegenerative disease, period. “If you remember nothing else, remember this: If you’re in your 40s or beyond and you care about living longer, which immediately puts you in a selection bias category, there’s an 80% chance you’re going to die of [one of] those four diseases. So any strategy toward increasing longevity has to be geared toward reducing the risk of those diseases as much as is humanly possible. “[For those who don’t know,] cerebrovascular disease would be stroke, and there’s two ways you can have a stroke.

The Singularity Is Near: When Humans Transcend Biology
by Ray Kurzweil
Published 14 Jul 2005

Theodore Modis, professor at DUXX, Graduate School in Business Leadership in Monterrey, Mexico, attempted to develop a "precise mathematical law that governs the evolution of change and complexity in the Universe." To research the pattern and history of these changes, he required an analytic data set of significant events where the events equate to major change. He did not want to rely solely on his own list, because of selection bias. Instead, he compiled thirteen multiple independent lists of major events in the history of biology and technology from these sources: Carl Sagan, The Dragons of Eden: Speculations on the Evolution of Human Intelligence (New York: Ballantine Books, 1989). Exact dates provided by Modis. American Museum of Natural History.

The Elements of Statistical Learning (Springer Series in Statistics)
by Trevor Hastie , Robert Tibshirani and Jerome Friedman
Published 25 Aug 2009

Information theory and an extension of the maximum likelihood principle, Second International Symposium on Information Theory, pp. 267–281. 700 References Allen, D. (1974). The relationship between variable selection and data augmentation and a method of prediction, Technometrics 16: 125–7. Ambroise, C. and McLachlan, G. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data, Proceedings of the National Academy of Sciences 99: 6562–6566. Amit, Y. and Geman, D. (1997). Shape quantization and recognition with randomized trees, Neural Computation 9: 1545–1588. Anderson, J. and Rosenfeld, E. (eds) (1988).

Seveneves
by Neal Stephenson
Published 19 May 2015

So in the rare cases when actual settlements of that type were constructed de novo, as here, they tended to be built so as to meet the expectations of people who their whole lives had been watching fiction serials about their Second Millennium precursors. Even so, there were some surprises. Not so much the fact that it was female-owned. That wasn’t uncommon in the adult entertainment industry, and anyway some selection bias was at work—they had chosen to sit down in this place because it didn’t feel as creepy to Kath Two and Ariane as some of the others. More unexpected was the fact that as many as half of the people in there were Indigens. Those who weren’t—ones who had come across the water from the ice slab floating offshore—were identifiable by haircut, clothing, and bearing.

Anathem
by Neal Stephenson
Published 25 Aug 2009

That was when I told Arsibalt about my conversation with Varax and Onali—as the male and female Inquisitors were called, according to the grapevine. “Inquisitors in disguise, hmm, I don’t think I’ve heard of that,” Arsibalt said. Gazing worriedly at the look on my face, he added: “Which means nothing. It is selection bias: Inquisitors who can’t be distinguished from the general populace would of course go unnoticed and unremarked on.” Somehow I didn’t find that very comforting. “They have to move about somehow,” Arsibalt insisted. “It never occurred to me to wonder how exactly. They can’t very well have their own special aerocraft and trains, can they?

Golden Holocaust: Origins of the Cigarette Catastrophe and the Case for Abolition
by Robert N. Proctor
Published 28 Feb 2012

Huff peppered his attack with amusing asides and anecdotes, lampooning spurious correlations like that between the size of Dutch families and the number of storks nesting on the rooftops—which proves not that storks bring babies but rather that people with large families tend to have large houses (which therefore attract more storks). Huff also pointed to the selection bias in the high rate of breast cancer among Chinese men compared to Chinese women—explainable by the reluctance of females to report their maladies. Senator Neuberger moderated the hearings and was flabbergasted by Huff’s remarks: “Do you honestly think there is as casual a relationship between statistics linking smoking with disease as there is about storks and Chinese and so on?”

Ashes to Ashes: America's Hundred-Year Cigarette War, the Public Health, and the Unabashed Triumph of Philip Morris
by Richard Kluger
Published 1 Jan 1996

Examples of such undertakings were a small grant to a radiologist at the University of Cape Town in South Africa to investigate “population pockets” with a high incidence of lung cancer and low consumption of cigarettes and a $5,000 grant to Yale Medical School biostatistician Alvan Feinstein, who had investigated what he said was a selection bias in hospital diagnoses of cancer patients who smoked. Feinstein now solicited money to pursue a “multi-variable analysis” of disease, contending in his grant application to Jacob, “If cigarette smoking is as harmful as has been alleged, it should not only ‘cause’ these diseases but should also make their manifestations and outcome worse” in smokers, but his preliminary findings did not show that.