Improving Public Interpretation of Probabilistic Test Results : Distributive Evaluations

Health-service users err in posttest probability evaluations. Here we document for the first time that they succeed when they reason about numbers of cases and make distributive evaluations. A sample of women interested in prenatal testing incorrectly evaluated the posttest probability that a given fetus had an anomaly, but, regardless of their numeracy level, they correctly apportioned the cases for and against that hypothesis. This finding shows that health-service users are not doomed to fail in dealing with single-case probabilities, and suggests that probabilistic data can be used effectively for communicating test results