👉 Linda's Math, introduced by Stanford economist Michael Kahneman and Amos Tversky, is a psychological experiment that challenges traditional economic assumptions by demonstrating how people's judgments and decisions are influenced by irrelevant information. In the classic study, participants were asked to rate the likelihood of Linda being involved in a particular social group (feminist) or a more general one (bank employee), based on two unrelated pieces of information: "Linda is a bank teller" and "Linda is a feminist." Despite the fact that being a feminist is more specific and less likely to be true than just being a bank teller, many people incorrectly concluded that Linda being a feminist made her more likely to be a feminist. This reveals how stereotypes and irrelevant details can skew probability judgments, leading to systematic biases in decision-making, a phenomenon known as the conjunction fallacy.
