review of Woo-Kyoung Ahn's book Thinking 101

I like reading books about cognitive biases periodically, even if it's mostly familiar material. I'm hopeful I'll eventually get better at detecting when they're clouding my judgment. (I don't think it's working yet.) This is a solid entry in the genre, though not my favorite.

Explanations. Ahn tries not just to describe the biases we have, but to explain why we have them. These explanations seem speculative but they are fascinating:

Strategies. Here are some of the book's suggestions for dealing with one's own biases. They seem sensible, though I was hoping for more.

Examples. Ahn uses several politically-charged examples. That makes sense; politics is perhaps the area of life where clear thinking is most important, since poor decisions hurt not just individuals but entire societies. But it's also risky. For most contentious issues, I don't think you can really cover all the relevant considerations in a short discussion. Pointing out that one side is committing a particular fallacy and declaring the other side the winner is overly simplistic, and problematic for two reasons. Those who don't already agree with you will focus on the weaknesses or omissions in your discussion, and may dismiss your entire work as biased. Those who do already agree with you may develop the illusion that getting the right answers is easy, and subconsciously think of cognitive biases as primarily tools for explaining why the other political party is so stupid, rather than as problems that almost certainly affect their own thinking too in ways that are extremely difficult to detect and overcome.

In particular, consider Ahn's discussion of confirmation bias causing underrepresentation of women in science. She establishes the problem by citing a single anecdote about an award ceremony, not statistics. She makes broad statements like "society believes that men are better at science than women" and "[w]hen male students say something insightful during a seminar or in a class, they receive more compliments than female students who say similar things" without providing citations. Then to establish that this is depriving society of scientific advances, she again uses just a single anecdote (about the fact that the first page of results when you do a search for "scientists who developed COVID-19 vaccine" mostly returns women). The whole section comes across not as a serious investigation of a hypothesis, but as a recitation of the first few things that came to mind in support of a conclusion the author already believed in. To be clear, I'm not saying she's wrong. But sloppy arguments are dangerous even when the conclusions are correct. If someone who is skeptical of gender discrimination in science reads this part of the book, I suspect it will make them more skeptical, by making it easier for them to assume that concerns about gender discrimination are typically rooted in lazy thinking.

An example I did like was how job interviews encourage overreliance on a single sample:

...given that face-to-face interactions are vivid, salient, concrete, and memorable, interviewers think they are observing who the candidate truly is, rather than a biased portrayal of the person tinted by random factors. And this impression of a small sample of qualities on exhibit that particular day can make the decision-makers ignore the records that more accurately reflect the candidate's skills, demonstrated over many years. A person who looks amazing and brilliant during an interview may not be as awesome once they are hired. Given regression toward the mean, that is what we should, to some extent, expect. And a person who didn't perform brilliantly in an interview ... could turn out to be a big catch the company missed.

Studies. Some interesting studies Ahn references: