Definition
The base rate fallacy is the systematic tendency to ignore the prior probability (base rate) of an event when assessing how likely it is given some new evidence. People focus on the salience of the evidence — a positive test, a behavioural cue, a forensic match — and forget that the underlying prevalence dominates the final probability.
Daniel Kahneman and Amos Tversky documented the fallacy in the 1970s. It is one of the most robust and consequential biases in human reasoning, with consequences in medicine, law, security screening, and finance.
Why it matters
How it works
The corrective is to apply Bayes' theorem: posterior = (likelihood × prior) / evidence. If a disease has base rate 0.1% and a test is 99% accurate, only about 9% of positive tests come from genuinely diseased people. The base rate (0.1%) divides the calculation; the test accuracy (99%) does not save you.
A reliable mental check is to imagine 10,000 cases: how many have the condition? Of those, how many test positive? Of the rest, how many false-positive? The ratio of true positives to all positives is the answer, and the base rate makes itself unavoidable.