correlation does not imply causation

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

The Book of Why: The New Science of Cause and Effect

by Judea Pearl and Dana Mackenzie  · 1 Mar 2018

stifle principles, methods, and tools. Readers do not have to be scientists to witness this prohibition. In Statistics 101, every student learns to chant, “Correlation is not causation.” With good reason! The rooster’s crow is highly correlated with the sunrise; yet it does not cause the sunrise. Unfortunately, statistics has fetishized

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this commonsense observation. It tells us that correlation is not causation, but it does not tell us what causation is. In vain will you search the index of a statistics textbook for an entry on

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concept of a correlation coefficient. GALTON AND THE ABANDONED QUEST It is an irony of history that Galton started out in search of causation and ended up discovering correlation, a relationship that is oblivious of causation. Even so, hints of causal thinking remained in his writing. “It is easy to see that

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decidedly piratical tendencies,’ as the dictionary has it!” he wrote in 1934. “I interpreted… Galton to mean that there was a category broader than causation, namely correlation, of which causation was only the limit, and that this new conception of correlation brought psychology, anthropology, medicine and sociology in large part into the

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have seemed simple to Wright but turned out to be revolutionary because it was the first proof that the mantra “Correlation does not imply causation” should give way to “Some correlations do imply causation.” FIGURE 2.7. Sewall Wright’s first path diagram, illustrating the factors leading to coat color in guinea pigs. D

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of Causation. And not a tentative step but a bold and decisive one! The following year Wright published a much more general paper called “Correlation and Causation” that explained how path analysis worked in other settings than guinea pig breeding. I don’t know what kind of reaction the thirty-year-old

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heroes, Karl Pearson and Francis Galton, attesting to the redundancy or even meaninglessness of the word “cause.” He concludes, “To contrast ‘causation’ and ‘correlation’ is unwarranted because causation is simply perfect correlation.” In this sentence he is directly echoing what Pearson wrote in Grammar of Science. Niles further disparages Wright’s entire methodology. He

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in any other way. Determining the relative importance of several factors was one such question. Another beautiful example of this can be found in his “Correlation and Causation” paper, from 1921, which asks how much a guinea pig’s birth weight will be affected if it spends one more day in the

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mathematics altogether and calculate p by cursory inspection of the diagram. But in 1920, this was the first time that mathematics was summoned to connect causation and correlation. And it worked! Wright calculated p to be 3.34 grams per day. In other words, had all the other variables (A, L, C

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of Bayesian conditioning: a magical transfer of information without causality. Our minds rebel at this possibility because from earliest infancy, we have learned to associate correlation with causation. If a car behind us takes all the same turns that we do, we first think it is following us (causation!). We next think

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, published posthumously in 1956, philosopher Hans Reichenbach made a daring conjecture called the “common cause principle.” Rebutting the adage “Correlation does not imply causation,” Reichenbach posited a much stronger idea: “No correlation without causation.” He meant that a correlation between two variables, X and Y, cannot come about by accident. Either one of the

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the piebald pattern of guinea-pigs. Proceedings of the National Academy of Sciences of the United States of America 6: 320–332. Wright, S. (1921). Correlation and causation. Journal of Agricultural Research 20: 557–585. Wright, S. (1983). On “Path analysis in genetic epidemiology: A critique.” American Journal of Human Genetics 35

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, Jerome, 175, 179–180, 183, 224, 341 Cornfield’s inequality, 175 Coronary Primary Prevention Trial, 252 correlation, 29 causation and, 5–6, 82–84 Galton on, 62–63 spurious, 69–72 See also association, collider bias “Correlation and Causation” (Wright, S.), 82 counterfactual analysis, 261–262 counterfactuals, 9–10 causal diagram for, 42–43

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, 115, 350 Wold, Herman, 244 would-haves, 329–336 Wright, Philip, 72, 250–252, 251 (fig.) Wright, Sewall, 5–6, 18, 244, 309 on causation, 79–81 “Correlation and Causation” by, 82 on developmental factors, 74–76, 75 (fig.) Fisher and, 85 guinea pigs of, 72–74, 74 (fig.), 222 on model

Wealth, Poverty and Politics

by Thomas Sowell  · 31 Aug 2015  · 877pp  · 182,093 words

the many millennia of human history, during which various peoples’ and nations’ relative achievements have changed greatly. Moreover, as statisticians have often pointed out, correlation is not causation— and, as was said years ago: “It is better to be roughly right than precisely wrong.”32 Whether considering cultural, geographic, political or other

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be collected. Others blame some factor with which negative outcomes are correlated— blaming crime on poverty, for example. Statisticians have long warned against confusing correlation with causation, but too often those warnings have been ignored. Even when there is in fact a causal relationship between two things, that by itself does not

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performances— a correlation that holds for all three groups— a finer breakdown of the data by family income among all three groups shows that correlation is not causation. This raises the question as to what other factor could be affecting both educational and economic outcomes. The evidence suggests that there are behavioral

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Catalan, 53, 90 Causation, v, 187 causal issues versus moral issues: viii, 6–7, 245, 347, 407 causation versus blame: vii, viii, 382–405 causation versus correlation: 12, 382, 383–384 causes versus conveyances: 382, 388–390 combinations and permutations of causes: 39, 160–161, 164–165, 311, 314 determinism: 8, 208

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–8, 20, 130, 169, 170, 221, 271, 312, 315, 342, 354, 363, 375, 407 Statistics, ix, 33, 245–246, 319–335, 344, 361, 408 correlation versus causation: 12, 382, 383–384 “disparate impact” statistics: 259, 341, 404 income statistics: 319–335, 361 origins of statistical data versus origins of causation: 173, 370

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

Mindware: Tools for Smart Thinking

by Richard E. Nisbett  · 17 Aug 2015  · 397pp  · 109,631 words

it isn’t, we’re particularly likely to overestimate the strength of the relationship. Correlation doesn’t establish causation, but if there’s a plausible reason why A might cause B, we readily assume that correlation does indeed establish causation. A correlation between A and B could be due to A causing B, B

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a dozen other variables, are not answerable by MRA. What nature hath joined together, multiple regression analysis cannot put asunder. No Correlation Doesn’t Mean No Causation Correlation doesn’t prove causation. But the problem with correlational studies is worse than that. Lack of correlation doesn’t prove lack of causation—and this mistake

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to ask whether one of those variables has an effect on a dependent variable independent of the effects of the other variable. Just as correlation doesn’t prove causation, absence of correlation fails to prove absence of causation. False-negative findings can occur using MRA just as false-positive findings do—because

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the schemas map directly onto conditional logic. These include, for example, the schema distinguishing between independent and dependent events and the principle that correlation doesn’t prove causation. The sunk cost principle and the opportunity cost principle are deductively valid and can be derived logically from the principles of cost-benefit analysis

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; unconscious versus consensus, expert construals; see also inference; interpretation Consumer Reports context; choice influenced by; cultural differences and; dispositional factors versus contractual schemas contradiction(s) correlation; causation versus; coding and; experimental evidence versus; illusory; of influences on mood cosmological constant; multiple regression analysis of; of test scores and IQ cost-benefit analysis

The Big Fat Surprise: Why Butter, Meat and Cheese Belong in a Healthy Diet

by Nina Teicholz  · 12 May 2014  · 743pp  · 189,512 words

the parallel rising lines of fat consumption and cancer rates. “Now I want to emphasize that this is a very strong correlation, but that correlation does not mean causation,” he said. “I don’t think anybody can go out today, and say that food causes cancer.” He urged more research. However, the

The Numbers Game: The Commonsense Guide to Understanding Numbers in the News,in Politics, and inLife

by Michael Blastland and Andrew Dilnot  · 26 Dec 2008  · 219pp  · 65,532 words

causes the other, and never more so than when the numbers or measurements seem to agree. This is the oldest fallacy in the book, that correlation proves causation, and also the most obdurate. And so it has been observed by smart researchers that overweight people live longer than thinner people, and therefore

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to go together doesn’t mean one brings about the other. This shouldn’t need saying, but it does, hourly. Get this wrong—mistake correlation for causation—and we flout one of the most elementary rules of statistics or logic. When we spot a fallacy of this kind lurking behind a claim

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more malaria in the East African highlands. QED. Convinced by these news stories from respectable broadcasters and newspapers? You shouldn’t be; they are all causation/correlation errors, made harder to spot by plausibility (at least to some). Plausibility is often part of the problem, encouraging us to skip more rigorous proof

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are on, you can get closer to something more important than this kind of conviction: understanding. It is a peculiar hazard, this tendency to confuse causation and correlation, which is (A) well known and well warned against, yet (B) simultaneously repeated ad nauseam, making it tempting to say that A causes B

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schools do better than girls in mixed schools, therefore single-sex schools are better for girls. What seems often to determine how easily we spot causation/correlation errors is how fast a better explanation comes to mind: thinking of decent alternatives slows conclusions and sows skepticism. Once again, imagination can take you

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a third factor that proves to be the genuine explanation: age in the first, house size in the second. That is one typical way for causation/correlation error to creep in. Two things change at the same time, but the reason lies in a third. Now we begin to see how it

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born within the same family is, as far as we know, just as likely to do best in an IQ test as the first. The causation/correlation mistake here has been to try to explain what happens across many families (richer, smaller ones tend to do better; larger, poorer ones not so

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against sea-level rise may actually add to coastal erosion.” When Friends of the Earth is cautious, reporters might also think twice. Untangling climatic causation from correlation is fiendishly hard. And though climate change may raise sea levels and make coastal erosion seriously worse in the future, it is hard to claim

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to settle for easy answers. There is one caveat. Here and there you will come across a tendency to dismiss almost all statistical findings as correlation-causation fallacy, a rhetorical cudgel, as one careful critic put it, to avoid believing any evidence. But we need to distinguish between casual associations often made

The Art of Statistics: How to Learn From Data

by David Spiegelhalter  · 2 Sep 2019  · 404pp  · 92,713 words

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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100 operations that a hospital conducts on under-1s over a four-year period.* 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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, and what other studies have shown, ideally in a meta-analysis. 8. 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. 9. How relevant is the

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. * But I still prefer the Bayesian approach. * The fall began soon after the start of Facebook, but the data cannot tell us whether this is correlation or causation. * This error, in combination with other criticisms, was claimed to change conclusions of the study, but this is strongly disputed by the original authors

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

The Case Against Education: Why the Education System Is a Waste of Time and Money

by Bryan Caplan  · 16 Jan 2018  · 636pp  · 140,406 words

The Better Angels of Our Nature: Why Violence Has Declined

by Steven Pinker  · 24 Sep 2012  · 1,351pp  · 385,579 words

Everydata: The Misinformation Hidden in the Little Data You Consume Every Day

by John H. Johnson  · 27 Apr 2016  · 250pp  · 64,011 words

The Panic Virus: The True Story Behind the Vaccine-Autism Controversy

by Seth Mnookin  · 3 Jan 2012  · 566pp  · 153,259 words

The Demon in the Machine: How Hidden Webs of Information Are Finally Solving the Mystery of Life

by Paul Davies  · 31 Jan 2019  · 253pp  · 83,473 words

Enlightenment Now: The Case for Reason, Science, Humanism, and Progress

by Steven Pinker  · 13 Feb 2018  · 1,034pp  · 241,773 words

The Stuff of Thought: Language as a Window Into Human Nature

by Steven Pinker  · 10 Sep 2007  · 698pp  · 198,203 words

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by David Spiegelhalter  · 14 Oct 2019  · 442pp  · 94,734 words

Numbers Rule Your World: The Hidden Influence of Probability and Statistics on Everything You Do

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The Signal and the Noise: Why So Many Predictions Fail-But Some Don't

by Nate Silver  · 31 Aug 2012  · 829pp  · 186,976 words

The Hype Machine: How Social Media Disrupts Our Elections, Our Economy, and Our Health--And How We Must Adapt

by Sinan Aral  · 14 Sep 2020  · 475pp  · 134,707 words

How Medicine Works and When It Doesn't: Learning Who to Trust to Get and Stay Healthy

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Big Data: A Revolution That Will Transform How We Live, Work, and Think

by Viktor Mayer-Schonberger and Kenneth Cukier  · 5 Mar 2013  · 304pp  · 82,395 words

The Health Gap: The Challenge of an Unequal World

by Michael Marmot  · 9 Sep 2015  · 414pp  · 119,116 words

The Great Mental Models: General Thinking Concepts

by Shane Parrish  · 22 Nov 2019  · 147pp  · 39,910 words

Brain Energy: A Revolutionary Breakthrough in Understanding Mental Health--And Improving Treatment for Anxiety, Depression, OCD, PTSD, and More

by Christopher M. Palmer Md  · 15 Nov 2022  · 402pp  · 107,908 words

The Blank Slate: The Modern Denial of Human Nature

by Steven Pinker  · 1 Jan 2002  · 901pp  · 234,905 words

Prophecy: Prediction, Power, and the Fight for the Future, from Ancient Oracles to AI

by Carissa Véliz  · 21 Apr 2026  · 503pp  · 129,255 words

10% Less Democracy: Why You Should Trust Elites a Little More and the Masses a Little Less

by Garett Jones  · 4 Feb 2020  · 303pp  · 75,192 words

Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth

by Stuart Ritchie  · 20 Jul 2020

End This Depression Now!

by Paul Krugman  · 30 Apr 2012  · 267pp  · 71,123 words

How to Read a Paper: The Basics of Evidence-Based Medicine

by Trisha Greenhalgh  · 18 Nov 2010  · 321pp  · 97,661 words

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by Heather Adkins, Betsy Beyer, Paul Blankinship, Ana Oprea, Piotr Lewandowski and Adam Stubblefield  · 29 Mar 2020  · 1,380pp  · 190,710 words

Architects of Intelligence

by Martin Ford  · 16 Nov 2018  · 586pp  · 186,548 words

Thinking, Fast and Slow

by Daniel Kahneman  · 24 Oct 2011  · 654pp  · 191,864 words

The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences

by Rob Kitchin  · 25 Aug 2014

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