The representativeness heuristic, described by Kahneman and Tversky (1972), involves judging the probability that an instance belongs to a category based on how representative (similar) it is of that category. A description of someone as quiet, organized, and detail-oriented is judged as more likely to be a librarian than a salesperson, because the description is more representative of the librarian stereotype — even when base rates (far more salespeople than librarians) favor the salesperson category.
Biases Produced
Representativeness leads to several systematic biases. Base rate neglect: ignoring the prior probability of categories. The conjunction fallacy: judging "Linda is a bank teller and a feminist" as more probable than "Linda is a bank teller," because the conjunction is more representative of Linda's description. Insensitivity to sample size: failing to appreciate that small samples produce more extreme outcomes. The gambler's fallacy: expecting random sequences to "look random" (after several heads, a tail is "due").
"Linda is 31, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice." Is it more probable that Linda is (a) a bank teller, or (b) a bank teller and active in the feminist movement? Over 80% choose (b), violating the conjunction rule of probability (a conjunction cannot be more probable than either of its constituents). This demonstrates how representativeness can override basic probabilistic reasoning.