Improving Public Interpretation of Probabilistic Test Results: Distributive Evaluations


  • Pighin Stefania
  • Gonzalez Michel
  • Savadori Lucia
  • Girotto Vittorio

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Health service users err in posttest probability evaluations. Here we document for the first time that users 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.

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