 More generally, for a wide range of cases where inductive reasoning is important, enumerative induction is inadequate. Rather, the kind of evidential reasoning that judges the likely truth of hypotheses  on the basis of what they say (or imply) about the evidence is more appropriate. Consider the kinds of inferences jury members are supposed to make, based on the evidence presented at a murder trial. The inference to probable guilt or innocence is based on a patchwork of evidence of various kinds. It almost never involves consideration of a randomly selected sequences of past situations when people like the accused committed similar murders. Or, consider how a doctor diagnoses her patient on the basis of his symptoms. Although the frequency of occurrence of various diseases when similar symptoms have been present may play a role, this is clearly not the whole story. Diagnosticians commonly employ a form of hypothesis evaluation—e.g., would the hypothesis that the patient has a brain tumor account for his symptoms?; or are these symptoms more likely the result of a minor stroke?; or may some other hypothesis better account for the patient’s symptoms? Thus, a fully adequate account of inductive logic should explicate the logic of hypothesis evaluation, through which a hypothesis or theory may be tested on the basis of what it says (or "predicts") about observable phenomena. In  Section 3  we will see how a kind of probabilistic inductive logic called "Bayesian Inference" or "Bayesian Confirmation Theory" captures such reasoning. The full logical structure of such arguments will be spelled out in that section.