Most psychological research on Bayesian reasoning since the 1970s has used a type of problem that tests a certain kind of statistical reasoning performance. The subject is given statistical facts within a hypothetical scenario. Those facts include a base-rate statistic and one or two diagnostic probabilities. The subject is meant to use that information to arrive at a “posterior” probability estimate. For instance, in one well-known problem (Eddy, 1982) the subject encounters the following: The probability of breast cancer is 1% for aw oman at age forty who participates in routine screening. If a woman has breast cancer, the probability is 80% that she will get a positive mammography. If a woman does not have breast cancer, the probability is 9.6% that she will also get a positive mammography. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer? __ %. The information in such problems can be mapped onto common expressions that use H as the focal hypothesis, ¬H as the mutually-exclusive hypothesis, and D as datum: P(H), the prior (often equated with the base-rate) probability of the hypothesis; P(D|H), the true-positive rate; and P(D|¬H), the false-positive rate. In the mammography problem, P(H) = 0.01, P(D|H) = 0.80, and P(D|¬H) = 0.096. Furthermore, P(¬H) = 1– P(H) = 0.99. The estimate queried is P(H|D). Bayes’ theorem states: