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Thinking Fast and Slow Part II: Heuristics and Biases


Part II explores some heuristics that lead human beings to systematic errors in thinking.

Human beings make quick judgments based on few observations (law of small numbers). This is related to things like the “hot hand” belief, as well as inferring causation when just random fluctuations are occurring (fighter pilot story). We also think the picture we get is more coherent and reliable than it is.

Without knowing it, having certain figures or levels mentioned subconsciously influences our evaluations. This is the anchoring effect. We’re primed with a figure, and then stray from that figure instead of making a neutral guess. Further, we don’t even recognize that we are being primed.

This is related to the availability heuristic. If we are asked a question about how likely something is, our system 1 minds look for how difficult it is for us to come up with examples of that event. The more examples, the more likely we judge it, hence the fear of airplanes and of kidnappers and other newsworthy items.

Next, there’s the representativeness heuristic, where we judge how likely something fits into a group (is Tom a librarian or a pro-football player?) by how much they represent the stereotypical character in that group. This ignores base rates though. There are very few pro-football players, and very many librarians, so even if Tom is described as hefty and athletic, he can still be considered more likely to be a librarian than a pro-football player.

Our ability to form causal stories greatly increases how likely we think certain scenarios are, and random statistical fluctuations that end up regressing to the mean are easily fitted with explanatory causes that are erroneous.


Kindle Notes:

The law of small numbers is a manifestation of a general bias that favors certainty over doubt (1902).

The exaggerated faith in small samples is only one example of a more general illusion—we pay more attention to the content of messages than to information about their reliability, and as a result end up with a view of the world around us that is simpler and more coherent than the data justify. Jumping to conclusions is a safer sport in the world of our imagination than it is in reality. Statistics produce many observations that appear to beg for causal explanations but do not lend themselves to such explanations. Many facts of the world are due to chance, including accidents of sampling. Causal explanations of chance events are inevitably wrong (1971).

The main moral of priming research is that our thoughts and our behavior are influenced, much more than we know or want, by the environment of the moment. Many people find the priming results unbelievable, because they do not correspond to subjective experience.Read more at location (2149).

the statistician David Freedman used to say that if the topic of regression comes up in a criminal or civil trial, the side that must explain regression to the jury will lose the case.Read more at location (3092).

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