Calling Bullshit: The Art of Scepticism in a Data-Driven World
by Jevin D. West and Carl T. Bergstrom · 3 Aug 2020
we read about recent studies in medicine or policy or any other area, these subtleties are often lost. It is a truism that correlation does not imply causation. Do not leap carelessly from data showing the former to assumptions about the latter.*4 This is difficult to avoid, because people use data
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, no bullshit. This is the right way to report the study’s findings. The Zillow article describes a correlation, and then uses this correlation to generate hypotheses about causation but does not leap to unwarranted conclusions about causality. Given that the study looks only at women aged 25 to 29, we might
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use the word “cause,” it does use the word “effect”—another way of suggesting causal relationships. Correlation doesn’t imply causation—but apparently it doesn’t sell newspapers either. If we have evidence of correlation but not causation, we shouldn’t be making prescriptive claims. NPR reporter Scott Horsley posted a tweet announcing that
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“Washington Post poll finds NPR listeners are among the least likely to fall for politicians’ false claims.” Fair enough. But this poll demonstrated only correlation, not causation. Yet Horsley’s tweet also recommended, “Inoculate yourself against B.S. Listen to NPR.” The problem with this logic is easy to spot. It
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to delay gratification does not necessarily cause later success and well-being.*6 But as these results filtered through the popular press, the line between correlation and causation became blurred. The results of the marshmallow test and other related studies were reported as evidence that ability to delay gratification causes success later
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we ever be confident that one thing causes another? Scientists struggle with this problem all the time, and often use manipulative experiments to tease apart correlation and causation. Consider the biology of fever. We commonly think of fever as something that disease does to us, the way a cold gives us a
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the body’s defenses against infection. For example, people who mount a fever are more likely to survive a bloodstream infection. But this is a correlation, not causation. Does fever cause better outcomes, as diagrammed below? Or are patients who are in better condition (healthier overall, not malnourished, with less severe infections
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other forms of evidence. That’s all good and well, but when you do so don’t be taken in by an unfounded leap from correlation to causation. *1 Linear correlations require variables with numerical values such as height and weight, whereas associations can occur between categorical values such as “favorite color
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catchy a phrase, it’s worth remembering that association does not imply causation either. That said, it is worth noticing that although correlation does not imply causation, causation does imply association. Causation may not generate a linear correlation, but it will generate some sort of association. *5 Geller and colleagues write: “It would be
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): Now, exposure to Roundup may well have serious health consequences. But whatever they may be, this particular graph is not persuasive. First of all, correlation is not causation. One would find a similar correlation between cell phone usage and thyroid cancer, for example—or even between cell phone usage and Roundup usage
Data Science from Scratch: First Principles with Python
by Joel Grus · 13 Apr 2015 · 579pp · 76,657 words
, but (depending on what you’re measuring) it’s quite possible that this relationship isn’t all that interesting. Correlation and Causation You have probably heard at some point that “correlation is not causation,” most likely by someone looking at data that posed a challenge to parts of his worldview that he was reluctant
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, grouping data into, Exploring One-Dimensional Data business models, Modeling C CAPTCHA, defeating with a neural network, Example: Defeating a CAPTCHA-Example: Defeating a CAPTCHA causation, correlation and, Correlation and Causation, The Model cdf (see cumulative distribtion function) central limit theorem, The Central Limit Theorem, Confidence Intervals central tendenciesmean, Central Tendencies median, Central
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, Correctness continue statement (Python), Control Flow continuity correction, Example: Flipping a Coin continuous distributions, Continuous Distributions control flow (in Python), Control Flow correctness, Correctness correlation, Correlationand causation, Correlation and Causation in simple linear regression, The Model other caveats, Some Other Correlational Caveats outliers and, Correlation Simpson's Paradox and, Simpson’s Paradox correlation function
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, Goodness of Fit, Standard Errors of Regression Coefficients-Regularization standard normal distribution, The Normal Distribution statistics, Statistics-For Further Exploration, Mathematicscorrelation, Correlationand causation, Correlation and Causation other caveats, Some Other Correlational Caveats Simpson's Paradox, Simpson’s Paradox describing a single dataset, Describing a Single Set of Datacentral tendencies, Central Tendencies dispersion, Dispersion testing
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Algebra Vectors Matrices For Further Exploration 5. Statistics Describing a Single Set of Data Central Tendencies Dispersion Correlation Simpson’s Paradox Some Other Correlational Caveats Correlation and Causation For Further Exploration 6. Probability Dependence and Independence Conditional Probability Bayes’s Theorem Random Variables Continuous Distributions The Normal Distribution The Central Limit Theorem
Everything Is Tuberculosis: The History and Persistence of Our Deadliest Infection
by John Green · 18 Mar 2025 · 158pp · 49,742 words
romanticization of TB also intersected with fashion for both men and women, although the interaction was complicated, and we must be careful not to conflate correlation with causation. It’s often been said, for example, that corsets sought to emulate the experience of consumption by being restrictive enough to limit women’s
Everydata: The Misinformation Hidden in the Little Data You Consume Every Day
by John H. Johnson · 27 Apr 2016 · 250pp · 64,011 words
3 Red State Blues: Averages and Aggregates—A Closer Look at Summary Statistics 4 Are You Smarter Than an iPhone-Using, Radiohead-Loving Republican?: Understanding Correlation Versus Causation 5 In Statistics We Trust: Is What You’re Seeing True? 6 Shrinking Africa: Misrepresentation and Misinterpretation 7 Spoonfed Data: When Cherry Picking Goes
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your results in a way you need to be aware of.46 4 Are You Smarter Than an iPhone-Using, Radiohead-Loving Republican? Understanding Correlation Versus Causation As any self-respecting parent will tell you, there’s a lot of pressure to make sure little Susie and Johnny are smarter than their
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just read a lot of studies and media reports that seem to draw the wrong conclusion from statistical analyses—specifically, reports and articles that confuse correlation with causation, and therefore, sometimes unintentionally, mislead the reader about the key takeaways. It’s important to note that there are two issues here: first of
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all, there are the original scientific studies that sometimes confuse correlation with causation. But what you’re more likely to encounter in your everyday life are newspaper articles and other media accounts that misreport the findings from valid
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chapter, the mere existence of such a statistical relationship between two factors does not imply that there is actually a meaningful link between them. Correlation does not equal causation. It’s actually one of the most common ways that people misinterpret data. But don’t worry—in this chapter, we’ll take
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a close look at how and why people mistake correlation for causation, and give you the tools to help you understand which everydata you should really believe. SMARTPHONES = SMART PEOPLE? So, back to the smart people
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VARIABLES All of these factors—town centers, sidewalks, Apple Stores—are possible omitted variables. An omitted variable is one of the primary reasons why correlation doesn’t equal causation. Remember when we talked about bivariate relationships—relationships between two variables? The problem is that often there are more than two variables. You
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? These are just a few of the countless questions that can be answered by identifying all the relevant omitted variables and finding the difference between correlation and causation. BE LIKE MIKE Correlation is a powerful tool for marketers and the media, especially when you combine it with our desire to be faster
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you can’t. Just because something worked for a celebrity doesn’t mean it will work for you. Assuming that it will is a classic correlation versus causation error. Another classic mistake? Jeffrey Brown—an economist and dean of the College of Business at the University of Illinois—offered this example when
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careers assessing and thinking about omitted variable bias, and it’s not uncommon for social scientists to spend hundreds of hours analyzing data to prove correlation or causation (or vice versa). We’re not trying to deter you—only to let you know the discipline and effort it can take to get
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relationship between people’s love for grilled cheese and their love lives. But there is no evidence that one causes the other. Hence, a correlation—but not causation. That said—from a purely statistical standpoint—you can’t always say that there isn’t causation just by looking at the data. In
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Law School student Tyler Vigen.30 Spurious correlations are useful because they highlight the existence of omitted variables and illustrate the potential danger of equating correlation with causation. We asked Vigen his opinions about different types of spurious correlations, and how people can do a better job interpreting them. “Take the oft
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perfect. But it gives us a framework for evaluating data in a scientific way. LOST IN TRANSLATION So why do many people get confused about correlation and causation? In some cases, the data is simplified, exaggerated, or misrepresented in some way. Remember the CNN article we talked about earlier: “Smarter people use
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Michigan professor of law J. J. Prescott explained, one big mistake most people make is due to “the easy, natural way the mind conflates causation with correlation. In news articles, this is almost always an issue, because stories about associations are just much less compelling than a story about causation. So, journalists
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weight gain?” the answer should reveal the true relationship between these two variables. HERE COMES THE SUN Perhaps another reason that so many people conflate correlation with causation is because of the way we’re hardwired to interpret data. “The human brain is a pattern-recognition machine,” explained Ron Friedman in an
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it doesn’t change the fact that people like having an explanation for things. We like understanding the world around us. Making the leap from correlation to causation gives us that understanding. But that doesn’t mean it’s the right way to interpret data. A SHOT IN THE DARK Consider the
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Jenny McCarthy talk about parents who say their baby got a fever, stopped speaking, and became autistic after being vaccinated.34 That’s a correlation—not causation. So why do one-third of parents surveyed believe that vaccinations can cause autism?35 The journal article widely credited with establishing a link has
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’s something to keep in mind anytime you’re interpreting data. When it comes to correlation versus causation, confirmation bias is one reason that some people ignore omitted variables—because they’re making the jump from correlation to causation based on preconceptions, not the actual evidence. LAST BUT NOT LEAST Even if you establish
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determine the exact correlation. But that’s okay, because our goal is simply to help you make better decisions by recognizing the difference between correlation and causation, and understanding some of the reasons that people confuse the two—so you can avoid making the same mistakes. How to Be a Good Consumer
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of Correlation and Causation So now, armed with a better understanding of the distinction between correlation and causation, here are some steps to keep in mind when consuming data about a statistical relationship: 1. Ask yourself
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not a cause-and-effect relationship, between coffee drinking and lower risk of endometrial cancer,” according to WebMD.35 In other words, there was correlation, but not causation. “P-hacking” (named after p-values) is a term used when researchers “collect or select data or statistical analyses until nonsignificant results become significant
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a classic issue in many studies of job performance. Finally, the authors of the veterans study also note their results are potentially picking up correlations but no causation. There are a lot of two-way analyses in this study—leadership correlated with athletics, self-confidence correlated with self-respect, etc. Here is
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not. Because when you wake up tomorrow morning, you will be bombarded with more data. More examples of sampling and cherry picking. More people confusing correlation for causation. More websites and blogs and newscasts, telling you what you should (and shouldn’t) do to live longer, get smarter, and be better. Hopefully
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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
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. Malkiel, “Returns from Investing in Equity Mutual Funds, 1971–1991,” Journal of Finance 50 (1995), 549–572. 24. Not to mention the distinction between causation and correlation, which we talked about in chapter 4. 25. Esteemed economist Daniel Kahneman shared the Nobel Prize in 2002 for his work related to psychological factors
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/home-values/) that offers predictions about the housing market. INDEX A ACNielsen, 25 Ad Contrarian, 96 advertising cherry-picking data for, 105–109, 117, 120 correlation vs. causation and, 53–54 targeted by zip code, 7 aggregates, 27–30 being a good consumer of, 42–43 definition of, 28 mean, median, mode
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Bush, Jeb, 68–69 C cancer coffee and endometrial, 75–76, 78 foods associated with, 76–77 secondhand smoke and, 69–70, 71–72 causation. See correlation and causation celebrity deaths, patterns of, 110–111 celebrity endorsements, 53 celiac disease, 21–22 Challenger space shuttle disaster, 9–13, 18, 73 charter schools
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Pediatrics, 18 CNN, 58 coffee, endometrial cancer and, 75–76, 78, 79 cognitive biases, 42–43 Coile, Courtney, 57 coincidence, 138–139. See also correlation and causation coins, flipping, 131–133 Colgate toothpaste, 117 common sense, 63 confidence, in forecasting, 139–140 confidence intervals, 74–75, 153 confidence levels, 74–75 confirmation
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–8, 24–25, 43–44, 62–63, 80–82, 102–103, 120–122, 140–141, 154–155 context, 6–7 Conwood Company, 41–42 correlation and causation, 45–63 advertising/marketing and, 53–54 being a good consumer of, 62–63 confirmation bias and, 61–62 data misrepresentation and, 58–60 failure
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high school athletes, 144–145 staying at the same job and, 36 incremental data, 91–92 inferences, 14–15 Institutional Investor’s Alpha, 135 intelligence correlation vs. causation and, 45–48 self-assessment of, 43 smartphones and, 46–48, 58 interpretation, 143–156. See also misrepresentation and misinterpretation brain’s hardwiring for
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, 75 memory of printed vs. online material, 2 Mercator, Gerardus, 83–85 misrepresentation and misinterpretation, 83–103. See also cherry-picking in charts, 87–92 correlation/causation based on, 58–60 data sources and, 99 errors and, 97–99 of food expiration dates, 99–100 in gas tank gauges, 96–97 guessing
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proxies, 49–50 psychology research, 15–16 publication bias, 80 p-values, 71, 72, 79 Q questions/questioning, 7–8 cherry-picking and, 122 correlation vs. causation, 60 of print vs. online information, 93–94 quote mining, 116 R Radio Television Digital News Association, 36 random chance, multiple comparison problem and, 75
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Rate My Professor, 51–52 Reagan, Ronald, 9 recall of printed vs. online material, 2 Reinhart, Carmen, 97–98 relationships, 5–6. See also correlation and causation bivariate, 47 dependence, 49 reverse causality, 53–54 statistical significance of, 69–72 Report of the Presidential Commission on the Space Shuttle Challenger Accident, 10
Rationality: What It Is, Why It Seems Scarce, Why It Matters
by Steven Pinker · 14 Oct 2021 · 533pp · 125,495 words
Reward (Rational Choice and Expected Utility) 7. Hits and False Alarms (Signal Detection and Statistical Decision Theory) 8. Self and Others (Game Theory) 9. Correlation and Causation 10. What’s Wrong with People? 11. Why Rationality Matters Notes References Index of Biases and Fallacies Index PREFACE Rationality ought to be the lodestar
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the basics of history, science, and the written word, they should command the intellectual tools of sound reasoning. These include logic, critical thinking, probability, correlation and causation, the optimal ways to adjust our beliefs and commit to decisions with uncertain evidence, and the yardsticks for making rational choices alone and with others
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from a rarer one.9 As we shall see, this is the essence of Bayesian reasoning. Another critical faculty exercised by the San is distinguishing causation from correlation. Liebenberg recalls: “One tracker, Boroh//xao, told me that when the [lark] sings, it dries out the soil, making the roots good to eat
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would happen if some circumstance were not true. It’s what allows us to think in abstract laws rather than the concrete present, to distinguish causation from correlation (chapter 9). The reason we say the rooster does not cause the sun to rise, even though one always follows the other, is that
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logic of impartiality. It also removes wicked temptations, sucker’s payoffs, and tragedies of mutual defection. 9 CORRELATION AND CAUSATION One of the first things taught in introductory statistics textbooks is that correlation is not causation. It is also one of the first things forgotten. —Thomas Sowell1 Rationality embraces all spheres of life, including
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in Ashgabat, we can identify the flaw in His Excellency’s advice. The president made one of the most famous errors in reasoning, confusing correlation with causation. Even if it were true that toothless Turkmens had not chewed bones, the president was not entitled to conclude that gnawing on bones is what
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in 1900 to find this as uproarious as he did, but if you get the joke at all, you can see how the difference between correlation and causation is part of our common sense. Nonetheless, Niyazovian confusions are common in our public discourse. This chapter probes the nature of
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correlation, the nature of causation, and the ways to tell the difference. What Is Correlation? A correlation is a dependence of the value of one variable on the value of
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gnats; through positive values where they splatter southwest to northeast; to 1, where they lie perfectly along the diagonal. Though the finger-pointing in correlation-versus-causation blunders is usually directed at those who leap from the first to the second, often the problem is more basic: no correlation was established in
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more bones don’t even have stronger teeth (r = 0). It’s not just presidents of former Soviet republics who fall short of showing correlation, let alone causation. In 2020 Jeff Bezos bragged, “All of my best decisions in business and in life have been made with heart, intuition, guts . . . not analysis
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of good fortune may be one of the reasons that life so often brings disappointment. What Is Causation? Before we lay out the bridge from correlation to causation, let’s spy on the opposite shore, causation itself. It turns out to be a surprisingly elusive concept.14 Hume, once again, set the
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stupid: to get into Harvard (B), you can be either rich (A) or smart (C). From Correlation to Causation: Real and Natural Experiments Now that we’ve probed the nature of correlation and the nature of causation, it’s time to see how to get from one to the other. The problem is not
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that “correlation does not imply causation.” It usually does, because unless the correlation is illusory or a coincidence, something must have caused one variable to align with the other. The problem
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a comparison across cable markets, the lower the channel number of Fox News relative to other news networks, the larger the Republican vote.29 From Correlation to Causation without Experimentation When a data scientist finds a regression discontinuity or an instrumental variable, it’s a really good day. But more often they
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to the other diagonal: the correlation between Democracy (the democracy score) at Time 2 and Peace (the peace score) at Time 1. This correlation captures any reverse causation, together with the confounds that have stayed put over the decade. If the first correlation (past cause with present effect) is stronger than the
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the preceding chapters. To be sure, many superstitions originate in overinterpreting coincidences, failing to calibrate evidence against priors, overgeneralizing from anecdotes, and leaping from correlation to causation. A prime example is the misconception that vaccines cause autism, reinforced by the observation that autistic symptoms appear, coincidentally, around the age at which children
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change, rather than for being steadfast warriors for the dogmas of their clique. Conversely, it could be a mortifying faux pas to overinterpret anecdotes, confuse correlation with causation, or commit an informal fallacy like guilt by association or the argument from authority. The “Rationality Community” identifies itself by these norms, but they
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of risks and benefits. Our intuitions about essences lead us to reject lifesaving vaccines and embrace dangerous quackery. Illusory correlations, and a confusion of correlation with causation, lead us to accept worthless diagnoses and treatments from physicians and psychotherapists. A failure to weigh risks and rewards lulls us into taking foolish risks
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. They found that people’s reasoning skills did indeed predict their life outcomes: the fewer fallacies in reasoning, the fewer debacles in life. Correlation, of course, is not causation. Reasoning competence is correlated with raw intelligence, and we know that higher intelligence protects people from bad outcomes in life such as illness
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, beginning with Immanuel Kant’s plan for “perpetual peace” in 1795. One of them is democracy, which, as we saw in the chapter on correlation and causation, really does reduce the chance of war, presumably because a country’s cannon fodder is less keen on the pastime than its kings and generals
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: a set of beliefs that includes a contradiction can be deployed to deduce anything and is perfectly useless. Wary as I must be of inferring causation from correlation, and of singling out just one cause in a crisscrossing historical mesh, I cannot claim that good arguments are the cause of moral progress
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; Pinker 2011, chap. 8; Trivers 1971. 20. Ridley 1997. 21. Ellickson 1991; Ridley 1997. 22. Hobbes 1651/1957, chap. 14, p. 190. CHAPTER 9: CORRELATION AND CAUSATION 1. Sowell 1995. 2. Cohen 1997. 3. BBC News 2004. 4. Stevenson & Wolfers 2008, adapted with permission of the authors. 5. Hamilton 2018. 6. Chapman
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, 311 collider fallacy, 261, 262–63 confirmation bias, 13–14, 142–43, 216, 290, 342n26 conjunction fallacy (Linda problem), 26–29, 115, 116, 156 correlation implies causation, 245–47, 251–52, 312, 321, 323–24, 329–30 data snooping, 145–46, 160 denying the antecedent, 83, 294 dieter’s fallacy, 101 discounting
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, 30, 78–80, 87–88, 308, 343n43 cooperation in the Prisoner’s Dilemma, 239–42 in Public Goods games, 242–44 coordination games, 233–35 correlation causation not implied by, 245–47, 251–52, 312, 321, 323–24, 329–30 coefficient (r), 250–51 cross-lagged panel correlation, 269–70 definition, 247
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“yellow journalism,” 125 See also media; pundits judicial system overview of classic illusions of, 321 accountability for lying and, 313 adversarial system of, 41, 316 correlation implying causation and, 260 death penalty, 221, 294, 311, 333 eyewitness testimony, 216, 219 fairness and, 217 false convictions, 216–21 forensic methods in, 216, 219
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–30, 131 media accountability for lying/disinformation, 313, 314, 316, 317 availability bias driven by, 120, 125–27 consumer awareness of biases in, 127 correlation confused with causation and, 256, 260, 353n13 cynicism bred by, 126–27 innumeracy of, 125–27, 314 negativity bias, 125–26 politically partisan, 296 rational choice portrayal
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–5, 321 science laureates and, 90 medicine base-rate neglect in diagnosis, 155 Bayesian reasoning in, 150–51, 152, 153–54, 167, 169–70, 321 correlation and causation, 251–52 COVID-19, 2, 283 disease control, 325 drug trials, 58, 264 evidence-based, 317 expected utility of treatments, 192–94, 198–99
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, 60 Niyazov, Saparmurat, 245–47, 251 Nobel Prize, 197, 327 noise. See Signal Detection Theory normative models of rationality, 7–8. See also Bayesian reasoning; causation; correlation; game theory; logic; probability; rational choice; Signal Detection Theory not, as logical connector, 75 See also complement of an event no true Scotsman fallacy, 88
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learning p-hacking, 145 Pizzagate conspiracy theory, 299, 302, 304 plane crashes as risk, 33, 120, 121, 122 Plato, Euthyphro, 67 poker, 231 police and correlation–causation confusion, 260 evidence-based evaluation of, 317 killing African Americans, 123, 124–25, 141 reporting concerns to, 299, 308 policy avoiding sectarian symbolism in, 312
How Medicine Works and When It Doesn't: Learning Who to Trust to Get and Stay Healthy
by F. Perry Wilson · 24 Jan 2023 · 286pp · 92,521 words
be a major source of medical misinformation and false medical beliefs. So how can you extricate mere correlation from true causation? Correlation Is Not Causation, But… You’ve no doubt heard the old adage that correlation is not causation. Spurious correlations abound, as the Maine-margarine example shows. Vigen’s website also notes the remarkable correlation
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randomization to assign the exposure of interest, and the vast majority of medical studies are not randomized. Therefore, the vast majority of medical studies assess correlation, not causation. They are, under the hood, an observation of data points that are examined mathematically for interesting relationships. Some of those correlations will be causal
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income, and with higher income comes a host of advantages to children, from access to better healthcare to private tutoring, and so on. Since correlation isn’t causation, changing your breastfeeding behavior won’t change your kid’s academic outcomes. And yet headlines will continue to tout the correlational findings—potentially misleading
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people into believing there is a causal link. Why this obsession with the distinction between correlation and causation in Medicine? Because if A causes B, changing A can change B. But if A is merely correlated with B, changing A will have
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some therapeutic change. That is because we are waiting to have a better understanding of the underlying causal process, if it even exists. The Biggest Correlation-Causation Disconnect of All Time Correlational studies often generate the data that supports more rigorous experimental studies, so in many cases we actually know for sure
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, changing certain patients from nonaspirin takers to aspirin takers really does reduce the chance of heart attack. This is causality. But the path from correlation to causation is not always so easily trod. And I cannot think of a substance with as profound a correlational track record and as weak a causal
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risk of various diseases, changing your vitamin D level doesn’t appear to lower that risk. This is the classic pattern we see when correlation is not causation. Why do we keep dipping into the correlational well with vitamin D? Several reasons. First, when correlations are strong, we are more likely to
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those things we can change together, and I become frustrated whenever causality is assigned to immutable characteristics—like age, sex, or race. Racial Medicine Is Correlation Without Causation One area of controversy when it comes to causality is the issue of race. Like vitamin D, race (and, particularly, Black race) shows up
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, referencing our oaths if necessary. We need to suggest alternative treatments with a stronger evidence base, answer questions, and be willing to explain how correlation and causation are different. We need to listen to our patients, to their experiences with past treatments and their concerns about future treatments, since all treatment is
May Contain Lies: How Stories, Statistics, and Studies Exploit Our Biases—And What We Can Do About It
by Alex Edmans · 13 May 2024 · 315pp · 87,035 words
first choice, but formula if Caspar needed a top-up or my wife wanted a break. Data is not enough Everyone knows the phrase ‘Correlation is not causation’, but not necessarily why. The breastfeeding studies show us exactly why. They did everything we’ve recommended so far. They started with a hypothesis
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, just common sense – but it’s often switched off when confirmation bias is at play. Why this matters Why is it so important to distinguish correlation from causation? Taken literally, study results are descriptions about the world. Breast-fed babies have better outcomes, people who start diets lose weight and more polluted
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them, how not to deal with them, and whether you even need to deal with them. There’s a second, and final, reason why correlation may not be causation, which we’ll now come to. When the tail wags the dog Nicotine patches, vaping, gum, hypnosis, acupuncture . . . even taking up knitting. There
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stopping smoking leads to a greater likelihood of dying.12 What’s going on here? Hopefully by now you’re on your guard that correlation needn’t be causation – it’s unlikely that giving up cigarettes drove the higher deaths. However, it’s not clear what the common causes might be. Most
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likelihood of quitting by such a large amount. The latter drowns out the former, leading to a positive correlation overall. In other cases, reverse causation strengthens the correlation rather than causing it to flip, so it makes you think there’s an effect when there isn’t. In The Spirit Level, inequality
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reverse causation is at play and data is not evidence. In a nutshell • Data is not evidence because it may not be conclusive. Correlation may not be causation due to common causes that drive both the input and the output. ◦ A mother’s IQ may increase both the likelihood of breastfeeding and
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would work against your results, i.e. drive the input and output in different directions to what you find. • A second reason why correlation is not causation is reverse causation : the output affects the input. Part II so far, and this chapter in particular, may have presented a bleak picture. It seems we
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higher in cities with greater school choice, for example Boston compared to Miami. If we’re a free marketer, we’re tempted to accept this correlation as causation – as evidence that competition causes higher performance. This is when our rational System 2 should kick in and remind us that there are two
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alternative explanations for any correlation. One is reverse causation. Poor child performance might reduce the number of districts: when districts are doing badly, they merge to cut costs. Another is common causes
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’t measure parents’ concern for education, so you can’t control for it. So what do we do? To understand how to move from correlation to causation, let’s first see what researchers did to solve an even more important question than our children’s education – one of life and death. How
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do, randomizing who gets what. If the input is arbitrarily assigned, nothing’s causing it, so there can’t be any common cause.‡ Then, correlation does imply causation, and so data is evidence. Why something is better than nothing RCTs are the gold standard in showing causation, and the methodology has evolved
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by differences in ability, because the researchers held ability constant and changed only the name. This randomization allowed them to demonstrate not an innocent correlation, but a harrowing causation. A shock to the system Given the success of RCTs across a variety of fields, you might hope to use them for the
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work in the economics of education and has had a significant influence on policy around the world, because the instrument allowed her to demonstrate causation, not just correlation. Blunt instruments Just as the ‘superfood’ craze encourages companies to peddle their goods as superfoods, unscrupulous researchers often dress up their work with the
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to some and a placebo to others. Since the input is exogenous (randomly assigned), any difference in output can be attributed to the input, and correlation is causation. • RCTs may be expensive, and unethical if the treatment might cause harm. If so, we can’t assign the input ourselves; we need to
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’t like triggers our amygdala, and we’re raring to demolish it. When such research is shared, naysayers will carelessly trot out the phrase ‘Correlation is not causation’ – without reading the article and seeing if the researchers addressed this concern. Often readers will criticize a study by starting with ‘I haven’t
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data is often a simple logic problem that doesn’t require any mathematical ability; this book hasn’t contained a single equation. Understanding that correlation doesn’t imply causation is as simple as being aware of alternative explanations, just as kids’ whodunit brainteasers have multiple potential culprits. Psychologists Geoffrey Fong, David Krantz
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authors have thrown away data and the results may not hold using the full measures. 3. Could the output have caused the input (reverse causation)? If investment is correlated with future performance, it might be that when companies have good future prospects, they’re more willing to invest – rather than investment causing
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incentives outperform CEOs with low equity incentives by 4–10% per year, and the researchers do further tests to suggest that the results are causation rather than correlation.’ † Shareholder value is how much value a company creates for its shareholders. If the company is publicly traded, its market value (how much it
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trials (RCTs) 174–5, 177 control samples 99, 102 controls 158 Cook, Scott 250 cooking the books 138–40 Cornell University Medical School 39 correlation 148, 166 causation and 148–54, 163–6, 173 common causes 170 inputs and outputs 162–3 reverse causation 167, 170 counterarguments 214–16 counterfactual 98 Covey
The Genetic Lottery: Why DNA Matters for Social Equality
by Kathryn Paige Harden · 20 Sep 2021 · 375pp · 102,166 words
? As I described in the previous chapter, a GWAS correlates small bits of DNA with an outcome, but, as is the common refrain—correlation does not equal causation. How do we get from the correlational results of GWAS to an understanding of how genes may be a cause of social inequalities in
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to that topic that we turn our attention in the next chapter. 5 A Lottery of Life Chances Every Psychology 101 student knows that “correlation does not equal causation.” Restaurants that add more grated sea urchin to every dish might be rated higher on Yelp, but that correlation does not mean that
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driven by being in foster care or being female. Part of the reason why every first-year undergraduate is told, at some point, that “correlation does not equal causation” is a variation on that point. Yes, volume of ice cream sales in a county are positively correlated with murder rates, but eating
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parents talk to their children will make a difference in how well those children do in school. We are back to the idea that correlation does not equal causation. The idea that genetic differences between people are braided together with the environmental differences that social scientists seek to understand and change can
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health problems, and that abstaining from sex will prevent these bad things from happening to them. There are problems, of course, with leaping from correlation to causation. Teenagers who have sex at fourteen are different from those who are still virgins at twenty-two, in lots of ways other than their sexual
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education are more likely to be diagnosed and get their tumour registered, an example of what is known as ascertainment bias in epidemiology. ‘Correlation Does Not Imply Causation’ We saw in the last chapter how Pearson’s correlation coefficient measures how close the points on a scatter-plot are to a straight
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long pedigree. When Karl Pearson’s newly developed correlation coefficient was being discussed in the journal Nature in 1900, a commentator warned that ‘correlation does not imply causation’. In the succeeding century this phrase has been a mantra repeatedly uttered by statisticians when confronted by claims based on simply observing that two
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not arise from an experiment, it is said to be observational. So often we are left with trying as best we can to sort out correlation from causation by using good design and statistical principles applied to observational data, combined with a healthy dose of scepticism. The issue of old men’s
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operations that a hospital conducts on under-1s over a four-year period.fn7 Of course, to use what is now rather a cliché, correlation does not mean causation, and we cannot conclude that bigger throughput is the reason for the better performance: as we mentioned previously, there could even be reverse
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data, and what other studies have shown, ideally in a meta-analysis. What’s the claimed explanation for whatever has been seen? Vital issues are correlation v. causation, regression to the mean, inappropriate claim that a non-significant result means ‘no effect’, confounding, attribution, prosecutor’s fallacy. How relevant is the story
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. CHAPTER 12: How Things Go Wrong 1 The fall began soon after the start of Facebook, but the data cannot tell us whether this is correlation or causation. 2 This error, in combination with other criticisms, was claimed to change conclusions of the study, but this is strongly disputed by the original
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Human + Machine: Reimagining Work in the Age of AI
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Surviving AI: The Promise and Peril of Artificial Intelligence
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Forward: Notes on the Future of Our Democracy
by Andrew Yang · 15 Nov 2021
The Biology of Belief: Unleashing the Power of Consciousness, Matter & Miracles
by Bruce H. Lipton · 1 Jan 2005 · 220pp · 66,518 words
Collapse
by Jared Diamond · 25 Apr 2011 · 753pp · 233,306 words
Magic Internet Money: A Book About Bitcoin
by Jesse Berger · 14 Sep 2020 · 108pp · 27,451 words
Rethinking the Economics of Land and Housing
by Josh Ryan-Collins, Toby Lloyd and Laurie Macfarlane · 28 Feb 2017 · 346pp · 90,371 words
Stuck: How the Privileged and the Propertied Broke the Engine of American Opportunity
by Yoni Appelbaum · 17 Feb 2025 · 412pp · 115,534 words
The Messy Middle: Finding Your Way Through the Hardest and Most Crucial Part of Any Bold Venture
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The Globotics Upheaval: Globalisation, Robotics and the Future of Work
by Richard Baldwin · 10 Jan 2019 · 301pp · 89,076 words
Upgrade
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The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma
by Mustafa Suleyman · 4 Sep 2023 · 444pp · 117,770 words
Straphanger
by Taras Grescoe · 8 Sep 2011 · 428pp · 134,832 words
In Defense of Global Capitalism
by Johan Norberg · 1 Jan 2001 · 233pp · 75,712 words
The New Kingmakers
by Stephen O'Grady · 14 Mar 2013 · 56pp · 16,788 words
Machine Learning for Email
by Drew Conway and John Myles White · 25 Oct 2011 · 163pp · 42,402 words
Do Nothing: How to Break Away From Overworking, Overdoing, and Underliving
by Celeste Headlee · 10 Mar 2020 · 246pp · 74,404 words
How the Railways Will Fix the Future: Rediscovering the Essential Brilliance of the Iron Road
by Gareth Dennis · 12 Nov 2024 · 261pp · 76,645 words
Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future
by Luke Dormehl · 10 Aug 2016 · 252pp · 74,167 words
Stories Are Weapons: Psychological Warfare and the American Mind
by Annalee Newitz · 3 Jun 2024 · 251pp · 68,713 words
Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity
by Daron Acemoglu and Simon Johnson · 15 May 2023 · 619pp · 177,548 words
Heart: A History
by Sandeep Jauhar · 17 Sep 2018 · 272pp · 78,876 words
Sun in a Bottle: The Strange History of Fusion and the Science of Wishful Thinking
by Charles Seife · 27 Oct 2009 · 356pp · 95,647 words
Free Ride
by Robert Levine · 25 Oct 2011 · 465pp · 109,653 words
Jennifer Morgue
by Stross, Charles · 12 Jan 2006
Red Moon
by Kim Stanley Robinson · 22 Oct 2018 · 492pp · 141,544 words
Apocalypse Never: Why Environmental Alarmism Hurts Us All
by Michael Shellenberger · 28 Jun 2020
Private Government: How Employers Rule Our Lives (And Why We Don't Talk About It)
by Elizabeth S. Anderson · 22 May 2017 · 205pp · 58,054 words
First Light: Switching on Stars at the Dawn of Time
by Emma Chapman · 23 Feb 2021 · 265pp · 79,944 words
How Cycling Can Save the World
by Peter Walker · 3 Apr 2017 · 231pp · 69,673 words
What’s Your Type?
by Merve Emre · 16 Aug 2018 · 384pp · 112,971 words
The Marginal Revolutionaries: How Austrian Economists Fought the War of Ideas
by Janek Wasserman · 23 Sep 2019 · 470pp · 130,269 words
The Economists' Hour: How the False Prophets of Free Markets Fractured Our Society
by Binyamin Appelbaum · 4 Sep 2019 · 614pp · 174,226 words
Green Swans: The Coming Boom in Regenerative Capitalism
by John Elkington · 6 Apr 2020 · 384pp · 93,754 words
Explaining Humans: What Science Can Teach Us About Life, Love and Relationships
by Camilla Pang · 12 Mar 2020 · 256pp · 67,563 words
Stress Test: Reflections on Financial Crises
by Timothy F. Geithner · 11 May 2014 · 593pp · 189,857 words
Alive
by Gabriel Weston · 15 Aug 2025 · 177pp · 59,831 words
The Hidden Family
by Charles Stross · 2 May 2005 · 344pp · 100,046 words
Road to Nowhere: What Silicon Valley Gets Wrong About the Future of Transportation
by Paris Marx · 4 Jul 2022 · 295pp · 81,861 words
The Ones We've Been Waiting For: How a New Generation of Leaders Will Transform America
by Charlotte Alter · 18 Feb 2020 · 504pp · 129,087 words
The Smartphone Society
by Nicole Aschoff
Fallen Idols: Twelve Statues That Made History
by Alex von Tunzelmann · 7 Jul 2021 · 337pp · 87,236 words
David Mitchell: Back Story
by David Mitchell · 10 Oct 2012 · 335pp · 114,039 words
The confusion
by Neal Stephenson · 13 Apr 2004 · 1,020pp · 339,564 words
The New Division of Labor: How Computers Are Creating the Next Job Market
by Frank Levy and Richard J. Murnane · 11 Apr 2004 · 187pp · 55,801 words
The Price Is Wrong: Why Capitalism Won't Save the Planet
by Brett Christophers · 12 Mar 2024 · 557pp · 154,324 words
The Driver in the Driverless Car: How Our Technology Choices Will Create the Future
by Vivek Wadhwa and Alex Salkever · 2 Apr 2017 · 181pp · 52,147 words
The Joy of Tax
by Richard Murphy · 30 Sep 2015 · 233pp · 71,775 words
Ghettoside: A True Story of Murder in America
by Jill Leovy · 27 Jan 2015 · 388pp · 119,492 words
The Achievement Habit: Stop Wishing, Start Doing, and Take Command of Your Life
by Bernard Roth · 6 Jul 2015 · 231pp · 73,818 words
Not One Inch: America, Russia, and the Making of Post-Cold War Stalemate
by M. E. Sarotte · 29 Nov 2021 · 791pp · 222,536 words