PSY 341 Lecture Notes - Lecture 27: Base Rate Fallacy, Mammography, Inductive Reasoning
8 Dec 2016
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Document Summary
Representativeness and base rate neglect: we often have two kinds of information available for decision-making. Information suggesting category membership: a type of representativeness/similarity judgment. Likelihood of membership in a category (probability: people often rely on heuristics and ignore base rate information/real probabilities and rely solely on diagnostic information. What is the rational way to use diagnostic and base rate information: we should apply the normative model for inductive reasoning. Bayes theorem: probability estimate that takes base rates and estimates for diagnostic information into account: concrete example to determine whether people make decisions about the likelihood of events that are consonant with statistical theory. How likely is it that julia has cancer if she has a positive mammogram: base rates. P (- ca) = . 99: bayes theorem conditional probabilities. P (+ mammogram/- ca) = . 10 (false alarm) P (+ mammogram/+ ca) x p (+ ca) + p (+ mammogram/- ca) x p (-ca)
