Welcome to the nonlinear library, where we use text-to-speech software to convert the best writing from the rationalist and ea communities into audio. This is: Biases: An Introduction, published by Rob Bensinger on the effective altruism forum. Imagine reaching into an urn that contains seventy white balls and thirty red ones, and plucking out ten mystery balls. Perhaps three of the ten balls will be red, and you’ll correctly guess how many red balls total were in the urn. Or perhaps you’ll happen to grab four red balls, or some other number. Then you’ll probably get the total number wrong. This random error is the cost of incomplete knowledge, and as errors go, it’s not so bad. Your estimates won’t be incorrect on average, and the more you learn, the smaller your error will tend to be. On the other hand, suppose that the white balls are heavier, and sink to the bottom of the urn. Then your sample may be unrepresentative in a consistent direction. That kind of error is called “statistical bias.” When your method of learning about the world is biased, learning more may not help. Acquiring more data can even consistently worsen a biased prediction. If you’re used to holding knowledge and inquiry in high esteem, this is a scary prospect. If we want to be sure that learning more will help us, rather than making us worse off than we were before, we need to discover and correct for biases in our data. The idea of cognitive bias in psychology works in an analogous way. A cognitive bias is a systematic error in how we think, as opposed to a random error or one that’s merely caused by our ignorance. Whereas statistical bias skews a sample so that it less closely resembles a larger population, cognitive biases skew our thinking so that it less accurately tracks the truth (or less reliably serves our other goals). Maybe you have an optimism bias, and you find out that the red balls can be used to treat a rare tropical disease besetting your brother, and you end up overestimating how many red balls the urn contains because you wish the balls were mostly red. Like statistical biases, cognitive biases can distort our view of reality, they can’t always be fixed by just gathering more data, and their effects can add up over time. But when the miscalibrated measuring instrument you’re trying to fix is you, debiasing is a unique challenge. Still, this is an obvious place to start. For if you can’t trust your brain, how can you trust anything else? Noticing Bias Imagine meeting someone for the first time, and knowing nothing about them except that they’re shy. Question: Is it more likely that this person is a librarian, or a salesperson? Most people answer “librarian.” Which is a mistake: shy salespeople are much more common than shy librarians, because salespeople in general are much more common than librarians—seventy-five times as common, in the United States.¹ This is base rate neglect: grounding one’s judgments in how well sets of characteristics feel like they fit together, and neglecting how common each characteristic is in the population at large.² Another example of a cognitive bias is the sunk cost fallacy—people’s tendency to feel committed to things they’ve spent resources on in the past, when they should be cutting their losses and moving on. Knowing about these biases, unfortunately, doesn’t make you immune to them. It doesn’t even mean you’ll be able to notice them in action. In a study of bias blindness, experimental subjects predicted that they would have a harder time neutrally evaluating the quality of paintings if they knew the paintings were by famous artists. And indeed, these subjects exhibited the very bias they had predicted when the experimenters later tested their prediction. When asked afterward, however, the very same subjects claimed that their assessments of the paintings had been objective and unaffected by the bias.³ Even when we correctly identify others’ biases, we exhibit a bias blind spot when it comes to ...