The meaning of malingering data: further applications of Bayes' theorem

A previous Behavioral Sciences and the Law article (Mossman & Hart, 1996) asserted that information from malingering tests is best conceptualized using Bayes' theorem, and that courts therefore deserve Bayesian interpretations when mental health professionals present evidence about malingering. Mossman and Hart gave several examples of estimated Bayesian posterior probabilities, but they did not systematically address how one constructs confidence intervals for these estimates. This article explains how the usually imperfect nature of humanly created diagnostic tests mandates Bayesian interpretations of test results, and describes methods for generating confidence intervals for posterior probabilities. Sample calculations show that Bayesian reasoning is quite feasible and would not require investigators to expend unusual efforts when constructing and validating malingering instruments. Bayesian interpretations most accurately capture what malingering tests do: provide information that alters one's beliefs about the likelihood of malingering.