The Representativeness heuristic states that we predict an uncertain event based on our prior knowledge of similarities in others of the same category. In other words, our judgment of an uncertain event is guided by the stereotypes or “prototypical exemplars” we have of similar events in our memory. The representativeness heuristic is involved when one says “She will win the election; you can see she is a winner” or “He won’t go far as an academic; too many tattoos.”
Representativeness Heuristics Can Lead to Errors in Judgment
However, be warned that our reliance on the Representativeness heuristic in decision making can cause errors since we often neglect more compelling relevant information such as base rate, accuracy of prior information, and other rules of logic and probability.
Join me in this now infamous experiment.
Linda is thirty-one years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations
Out of the possible scenarios, which is more likely?
Linda is a teacher in elementary school.
Linda works in a bookstore and take yoga classes.
Linda is active in the feminist movement.
Linda is a psychiatric social worker.
Linda is a member of the League of Women Voters.
Linda is a bank teller.
Linda is an insurance salesperson.
Linda is a bank teller and is active in the Feminist movement.
85% of the Stanford MBA students asked this question ranked “Linda is a bank teller and active in the Feminist movement” as more likely than “Linda is a bank teller.”
How Relying on Representativeness Leaves You Prone to Basic Logic Errors
The error made by the respondents in the Linda experiment was failing to take into account that the probability of one event occurring is always greater than the probability of that same event plus an additional detail.  Therefore the probability that Linda is a bank teller must be higher than the probability that she is a bank teller and active in the feminist movement.
It cannot be said that the MBA level students at Stanford were unaware of such a basic rule of probability. However, the description of Linda and it’s representativeness of the common stereotype of feminists won the battle against the rules of logic and probability.
A simpler version of this question showed only 2 choices:
Linda is a bank teller.
Linda is a bank teller and active in the feminist movement.
Yet still 90% of undergraduates surveyed picked “Linda is a bank teller and active in the feminist movement,” despite the violation of rules of logic and rationality. Such results shouldn’t come as a surprise. After all, we are associative machines, not machines of logic and probability.