In probability and statistics, base rate generally refers to the (base) class probabilities unconditioned on featural evidence, frequently also known as prior probabilities. For example, if it were the case that 1% of the public are "medical professionals" and 99% of the public are not "medical professionals," then the base rates in this case are 1% and 99%, respectively.
Naturally, in assessing the probability that a given individual is a member of a particular class, we must account for other information besides the base rate. In particular, we must account for featural evidence. For example, when we see a person wearing a white doctor's coat and stethoscope, and prescribing medication, we have evidence which may allow us to conclude that the probability of this particular individual being a "medical professional" is considerably greater than the category base rate of 1%.
The normative method for integrating base rates (prior probabilities) and featural evidence (likelihoods) is given by Bayes rule. A large number of psychological studies have examined a phenomenon called base-rate neglect in which category base rates are not integrated with featural evidence in the normative manner.
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