Statistics in a Nutshell
by Sarah Boslaugh · 10 Nov 2012
expect random error to affect an individual’s score consistently in one direction or the other. Random error makes measurement less precise but does not systematically bias results because it can be expected to have a positive effect on one occasion and a negative effect on another, thus canceling itself out over
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diameter one µm (micrometer), any bacteria smaller than this will not be part of the population that you are observing. This sampling limitation will introduce systematic bias into the study; however, as long as you are clear that the population about which you can make inferences is bacteria of diameter greater than
Thinking, Fast and Slow
by Daniel Kahneman · 24 Oct 2011 · 654pp · 191,864 words
common basis. On the other hand, the errors that individuals make are independent of the errors made by others, and (in the absence of a systematic bias) they tend to average to zero. However, the magic of error reduction works well only when the observations are independent and their errors uncorrelated. If
The Art of Statistics: Learning From Data
by David Spiegelhalter · 14 Oct 2019 · 442pp · 94,734 words
evidence is actually worth. For example, intensive analysis of data sets derived from routine data can increase the possibility of false discoveries, both due to systematic bias inherent in the data sources and from carrying out many analyses and only reporting whatever looks most interesting, a practice sometimes known as ‘data-dredging
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occasion, and so being a precise or repeatable number. Valid, in the sense of measuring what you really want to measure, and not having a systematic bias. Figure 3.1 Process of inductive inference: each arrow can be interpreted as ‘tells us something about’1 For example, the adequacy of the sex
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we hadn’t done X?’). We are a long way from AI having this ability. This book emphasizes the classic statistical problems of small samples, systematic bias (in the statistical sense) and lack of generalizability to new situations. The list of challenges for algorithms shows that although having masses of data may
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is conditional not only on the truth of the null hypothesis, but also on all other assumptions underlying the statistical model, such as lack of systematic bias, independent observations, and so on. This whole process has become known as Null Hypothesis Significance Testing (NHST) and, as we shall see below, it has
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with a control group. What is the statistical uncertainty / confidence in the findings? Check margins of error, confidence intervals, statistical significance, sample size, multiple comparisons, systematic bias. Is the summary appropriate? Check appropriate use of averages, variability, relative and absolute risks. HOW TRUSTWORTHY IS THE SOURCE? How reliable is the source of
The Data Detective: Ten Easy Rules to Make Sense of Statistics
by Tim Harford · 2 Feb 2021 · 428pp · 103,544 words
, 149, 151, 154 and Google Flu Trends, 153–57 vs. human judgment, 167–71 pattern-recognizing, 183 and proliferation of big data, 157–59 and systematic bias, 166 and teacher evaluations, 163–64 trustworthiness of, 179–82 See also big data Allegory of Faith, The (Vermeer), 29–30 Allied Art Commission, 21
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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
How to Read a Paper: The Basics of Evidence-Based Medicine
by Trisha Greenhalgh · 18 Nov 2010 · 321pp · 97,661 words
ethical considerations References Chapter 4: Assessing methodological quality Was the study original? Whom is the study about? Was the design of the study sensible? Was systematic bias avoided or minimised? Was assessment ‘blind’? Were preliminary statistical questions addressed? Summing up References Chapter 5: Statistics for the non-statistician How can non-statisticians
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?’). 6. The sample size was too small (see section ‘Were preliminary statistical questions addressed?’). 7. The study was uncontrolled or inadequately controlled (see section ‘Was systematic bias avoided or minimised?’). 8. The statistical analysis was incorrect or inappropriate (see Chapter 5). 9. The authors have drawn unjustified conclusions from their data. 10
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benefit of brown rice over white rice in the treatment of beriberi [8].) The problems of non-random allocation are discussed further in section ‘Was systematic bias avoided or minimised?’ in relation to determining whether the two groups in a trial can reasonably be compared with one another on a statistical level
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section ‘Three preliminary questions to get your bearings’). 4. Potentially eradicates bias by comparing two otherwise identical groups (but see subsequent text and section ‘Was systematic bias avoided or minimised?’). 5. Allows for meta-analysis (combining the numerical results of several similar trials) at a later date; see section ‘Ten questions to
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case–control study is the precise definition of who counts as a ‘case’, because one misallocated individual may substantially influence the results (see section ‘Was systematic bias avoided or minimised?’). In addition, such a design cannot demonstrate causality—in other words, the association of A with B in a case–control study
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. Does the prone sleeping position increase the risk of cot death (sudden infant death syndrome)? Does whooping cough vaccine cause brain damage? (see section ‘Was systematic bias avoided or minimised?’). Do overhead power cables cause leukaemia? Cross-sectional surveys We have probably all been asked to take part in a survey, even
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(because you can't fault the methods at all). These five questions—was the study original, whom is it about, was it well designed, was systematic bias avoided (i.e. was the study adequately ‘controlled’) and was it large enough and continued for long enough to make the results credible—are considered
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) in recent years is the emerging science of patient-reported outcomes measures, which I cover in the section ‘PROMs’ on page 223. Was systematic bias avoided or minimised? Systematic bias is defined by epidemiologists as anything that erroneously influences the conclusions about groups and distorts comparisons [4]. Whether the design of a study is
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of times by the same assessors, using the same outcome measures [5] [6]. Different study designs call for different steps to reduce systematic bias. Randomised controlled trials In an RCT, systematic bias is (in theory) avoided by selecting a sample of participants from a particular population and allocating them randomly to the different groups
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this ruling has subsequently been challenged, the principle stands—that assignment of ‘caseness’ in a case–control study must be performed rigorously and objectively if systematic bias is to be avoided. Was assessment ‘blind’? Even the most rigorous attempt to achieve a comparable control group will be wasted effort if the people
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terms of its: methodological quality—that is, extent to which the design and conduct are likely to have prevented systematic errors (bias) (see section ‘Was systematic bias avoided or minimised?’); precision—that is, a measure of the likelihood of random errors (usually depicted as the width of the confidence interval around the
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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
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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
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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
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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
Adaptive Markets: Financial Evolution at the Speed of Thought
by Andrew W. Lo · 3 Apr 2017 · 733pp · 179,391 words
curves, 29, 30, 31–33, 34 Surowiecki, James, 5, 16 survey research, 40 Sussman, Donald, 237–238 swaps, 243, 298, 300 Swedish Twin Registry, 161 systematic bias, 56 systematic risk, 194, 199–203, 204, 205, 250–251, 348, 389 systemic risk, 319; Bank of England’s measurement of, 366–367; government as
Infotopia: How Many Minds Produce Knowledge
by Cass R. Sunstein · 23 Aug 2006
point that bears on the foundations of democracy itself. But accuracy is likely only under identifiable conditions, in which people do not suffer from a systematic bias that makes their answers worse than random. If we asked everyone in the world to estimate the population of Egypt, or to say how many
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can identify two situations in which the judgment of a statistical group will be wrong. The first are those in which group members show a systematic bias. The second, a generalization of the first, are those in which their answers are worse than random. The failures of statistical judgments in these circumstances
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by private and public institutions. Often statistical groups will be wrong. Sometimes they will be disastrously wrong. The (Occasional) Power of Numbers / 33 Bias / A systematic bias in one or another direction will create serious problems for the group’s answers. Suppose that a large number of Nazis were asked to answer
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depends on whether the relevant experts were in a position to offer good answers on the questions at hand. If the experts suffer from a systematic bias, or if their answers are worse than random, any effort to aggregate expert judgments will produce blunders. Maybe we shouldn’t trust the people who
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it might be too thin simply because most institutions will have few investors;53 another is that members of the organization might suffer from a systematic bias. Aware of these risks, an institution might create a public market, available to all, believing that through this route it will obtain more accurate results
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, by 2100, to global warming. Why should we trust the average answer? Where people’s answers are worse than random, and where there is a systematic bias, the average answer is not going to be accurate. As I have emphasized, markets have many advantages over surveys because they create incentives for people
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Sun Microsystems, 171 Supreme Court, U.S., 134–35, 136, 184 Surowiecki, James, 21 surveys, 8, 198–200 synergy, deliberating groups and, 54–55, 220 systematic bias, 33–36, 41, 132, 199, 219 tennis, 140 terrorism, 29 individual rights and, 214 predictions market and, 107–9, 142–43 group polarization and, 93
Beyond Diversification: What Every Investor Needs to Know About Asset Allocation
by Sebastien Page · 4 Nov 2020 · 367pp · 97,136 words
bps—a fraction of our 300 bps of precost alpha. We left it to the reader to interpret whether this slight excess beta constitutes a systematic bias, but if so, the impact remains small relative to the magnitude of the net alphas. Regarding liquidity, our outsiders have a similar liquidity profile, on
Bad Pharma: How Medicine Is Broken, and How We Can Fix It
by Ben Goldacre · 1 Jan 2012 · 402pp · 129,876 words
you’d expect to see, over time, in any trial. Such rules are useful because they restrict the intrusion of human judgement, which can introduce systematic bias. But whatever we do about early stopping in medicine, it will probably pollute the data. A review from 2010 took around a hundred truncated trials
Finding Alphas: A Quantitative Approach to Building Trading Strategies
by Igor Tulchinsky · 30 Sep 2019 · 321pp
categories. We conclude with some practical suggestions for quantitative practitioners and firms. CATEGORIES OF BIAS We broadly categorize bias as systematic or behavioral. Investors introduce systematic bias by inadvertently coding it into their quantitative processes. By contrast, investors introduce behavioral bias by making ad hoc decisions rooted in their own human behavior
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. Over a period of time, both systematic and behavioral bias yield suboptimal investment outcomes. SYSTEMATIC BIASES There are two important sources of systematic bias: look-ahead bias and data mining. Finding Alphas: A Quantitative Approach to Building Trading Strategies, Second Edition. Edited by Igor Tulchinsky et al. and WorldQuant
Gaza: An Inquest Into Its Martyrdom
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Bad Science
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The Undoing Project: A Friendship That Changed Our Minds
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Understanding Asset Allocation: An Intuitive Approach to Maximizing Your Portfolio
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The Rationalist's Guide to the Galaxy: Superintelligent AI and the Geeks Who Are Trying to Save Humanity's Future
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The Half-Life of Facts: Why Everything We Know Has an Expiration Date
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The Future of Ideas: The Fate of the Commons in a Connected World
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Basic Income: A Radical Proposal for a Free Society and a Sane Economy
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Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
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Beautiful Data: The Stories Behind Elegant Data Solutions
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Misbehaving: The Making of Behavioral Economics
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Democratizing innovation
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Good Economics for Hard Times: Better Answers to Our Biggest Problems
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The Knowledge Machine: How Irrationality Created Modern Science
by Michael Strevens · 12 Oct 2020
Escape From Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It
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Hello World: Being Human in the Age of Algorithms
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Nixonland: The Rise of a President and the Fracturing of America
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Corbyn
by Richard Seymour
Sex, Lies, and Pharmaceuticals: How Drug Companies Plan to Profit From Female Sexual Dysfunction
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Market Risk Analysis, Quantitative Methods in Finance
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Statistics hacks
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Plenitude: The New Economics of True Wealth
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Lost in Math: How Beauty Leads Physics Astray
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The Alpha Masters: Unlocking the Genius of the World's Top Hedge Funds
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Forward: Notes on the Future of Our Democracy
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The Future of War
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Big Data: A Revolution That Will Transform How We Live, Work, and Think
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Fortunes of Change: The Rise of the Liberal Rich and the Remaking of America
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Delete: The Virtue of Forgetting in the Digital Age
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The Greed Merchants: How the Investment Banks Exploited the System
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