BaseRateNeglect

BaseRateNeglect, also known as base-rate bias or base-rate fallacy, is a cognitive bias in which people ignore base rates—the prior probabilities of outcomes in a population—when judging the likelihood of events after receiving new information. Instead, they overweight diagnostically salient information that seems representative of a category, leading to probability estimates that conflict with Bayes' rule.

The effect arises in part from the representativeness heuristic, with people focusing on how well new evidence matches a mental stereotype rather than on how common the underlying condition is in the population.

A classic illustration involves medical testing. In a population with low disease prevalence, a test with high sensitivity and specificity can still yield many false positives. For example, with 1% prevalence, 90% sensitivity, and 99% specificity, the probability of actually having the disease after a positive result is about 47–48%, not 90%.

Base-rate neglect can influence judgments in medicine, finance, law, and everyday decision making. Remedies include presenting base rates clearly, using natural frequencies rather than abstract probabilities, and training in Bayesian reasoning to align judgments more closely with probabilistic principles.

The concept is widely discussed in cognitive psychology, where Kahneman, Tversky, and colleagues showed that people often disregard base rates in favor of salient, diagnostic information. It is also known as base-rate bias or base-rate fallacy in various contexts.