The particular issue with which this paper is concerned is diagnostic inference.That is, given the occurrence of a set of outcomes/results/ symptoms, one has to infer to what extent is a particular action or event responsible for the observed effects. Einhorn and Hogarth (1982) argued that the essential aspects of such inferences are that they are causal rather than correlational, backward rather than forward (one goes from effects to prior causes), concerned with specific rather than the general cases, and constructive rather than nonconstructive (one can synthesize, enlarge, or otherwise develop new hypotheses). They further argued that the most common statistical model (e.g., Peterson & Beach, 1967) involving inferences does not consider these four aspects, and they developed a new model to describe how people assess the likelihood that one of two hypotheses is true on the basis of varying amount of evidence for each. I shall show, however, that their claims against the usual statistical model are unfounded and that they, in fact, misconceive the type of statistical problem with which they are faced.They think they are dealing with point estimation problems, when, in fact, the diagnostic problems with which they are dealing are Bayesian problems.Furthermore, even though they concluded that their model fitted the data reasonably well, some methodological considerations provide questions about their conclusion. The main purpose of this paper is to critique Einhorn and Hogarth's arguments and model in statistical and methodological terms.