Summary

When there are 2 independent probabilities of the same event, the base rate heuristic is to ignore or give less weight to the prior probability or base rate than to the likelihood probability. Tversky and Kahneman's normative rule is to combine the 2 independent probabilities, using the Bayes method.

Both the examination problem and the cab problem show that the base rate is less neglected when it is seen to be causal than when it is merely statistical. In the professions problem, neglect of the base rate is reduced by emphasizing the base rate and by reducing the emphasis on the likelihood probability. In both the cab problem and the professions problem, complete neglect of the base rate can be produced by committing a common logical fallacy that is unrelated to the base rate. The medical diagnosis problem shows that the neglect of the base rate can produce many false positives. The neglect can be greatly reduced by avoiding the use of undefined statistical terms, percentages, chance, and time stress if present.

Neglect of the base rate

A base rate describes the distribution of a characteristic in a population. An example of a causal base rate is the pass rate of an examination. The base rate is causal because it depends on the difficulty of the examination. An example of a noncausal or statistical base rate is the selected proportion of candidates in a sample who pass or fail an examination.