Many attempts have been made to support clinical decisions by formal statistical reasoning, but the practical impact of these efforts has been limited. Developers of “expert systems”, who use the techniques of artificial intelligence to represent clinicians' personal “knowledge”, have suggested one reason for this lack of success may be that the probabilistic methodology itself is often inappropriate to the clinical problems or opaque to the user. We contrast the statistical and “knowledge‐based” paradigms, with an emphasis on their different approaches to the manipulation and explanation of uncertainty. A statistical application to the diagnosis of “dyspepsia” is described, in which data are obtained by computer interview of the patient, and both diseases and symptoms form a hierarchy. We argue that the flexible use of “weights of evidence” overcomes many of the previous criticisms of statistical systems while retaining a valid probabilistic output. We conclude by discussing the complementary roles of deductive and probabilistic reasoning.

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