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Book Review: “The Seven Deadly Sins of Psychology”

This book review is a translated and slightly adjusted version of one that is currently in press for “De Psycholoog”. The review was inspired by the recent Dutch translation De 7 Doodzonden van de Psychologie (see references below).

In his inaugural address on September 11th 2001, Diederik Stapel made a bold claim about the prestige and accomplishments of the field of social psychology: “Physics may have crowned itself king, but social psychology is queen”. The notion of psychological science as an infallible source of knowledge returns in Daniel Kahneman’s bestseller Thinking, Fast and Slow. Specifically, Kahneman praises psychological research on the phenomenon of behavioral priming — a famous example of such priming is that people supposedly walk more slowly after reading words such as “grey” and “false teeth”; these words activate the concept “elderly” which in turn is associated with walking more slowly. Kahneman may be impressed with this type of work, but my experience is that, upon being confronted with priming research, most audiences start to laugh. Perhaps Kahneman shares this experience, for he writes: “When I describe priming studies to audiences, the reaction is often disbelief (…) The idea you should focus on, however, is that disbelief is not an option. The results are not made up, nor are they statistical flukes. You have no choice but to accept that the major conclusions of these studies are true” (pp. 56-57). Back in the day, critique on the scientific status quo was not appreciated: when a full professor at the University of Amsterdam once suggested that certain subdisciplines of psychology were better off being bulldozed, he ended up having to apologize profusely to his deeply offended colleagues. Lese-majesty!

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Addressing Elizabeth Loftus’ Lament: When Peeking at Data is Guilt-Free

Elizabeth Loftus is one of the world’s most influential psychologists and I have the greatest respect for her and her work. Several years ago we attended the same party and I still recall her charisma and good sense of humor. Also, Elizabeth Loftus studied mathematical psychology in Stanford, and that basically makes us academic family.

But…just as Stanford math psych graduate Rich Shiffrin, Elizabeth Loftus appears less than enthusiastic about the recent turning of the methodological screws. Below is an excerpt from a recent interview for the Dutch journal De Psycholoog (the entire interview can be accessed, in Dutch, here. I back-translated the relevant fragment from Dutch to English:

Vittorio Busato, the interviewer: “What is your opinion on the replication crisis in psychology?”
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The Man Who Rewrote Conditional Probability

The universal notation for “the probability of A given B” is p(A | B). We were surprised to learn that the vertical stroke was first introduced by none other than… Sir Harold Jeffreys! At least Jeffreys himself seems to think so, and the sentiment is echoed on the website “Earliest Uses of Symbols in Probability and Statistics” Specifically, on page 15 of his brilliant book “Scientific Inference” (1931), Jeffreys introduces the vertical stroke notation:


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Dennis Lindley’s Second Paradox

What is commonly referred to as “Lindley’s paradox” exposed a deep philosophical divide between frequentist and Bayesian testing, namely that, regardless of the prior distribution used, high-N data that show a significant p-value may at the same time indicate strong evidence in favor of the null hypothesis (Lindley, 1957). This “paradox” is due to Dennis Lindley, one of the most brilliant and influential scholars in statistics.1

Lindley was thoroughly and irrevocably a Bayesian, never passing on the opportunity of being polemic. For example, he argued that “the only good statistics is Bayesian statistics” (Lindley, 1975) or that Bradley Efron, who just received a big price, may have been “falling over all those bootstraps lying around” (Lindley, 1986). He also trashed Taleb’s Black Swan in great style. Somewhat surprisingly, he also took issues with the Bayes factor.2

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