Null hypotheses are simple, precise, and theoretically important. Conventional statistical analysis cannot support them; Bayesian analysis can. The challenge in a Bayesian analysis is to formulate a suitably vague alternative, because the vaguer the alternative is (the more it spreads out the unit mass of prior probability), the more the null is…

Argues that selecting data according to expected information gain, as proposed by M. Oaksford and N. Chater (1994, 1996), leads to suboptimal performance in Bayesian hypothesis testing. Procedures are presented that are better justified normatively, their psychological implications are explored, and a number of novel predictions are derived under…

Argues that Klauer's proposal (1999) and proposal presented are equally well justified from a normative perspective and that, where the predictions of the two approaches diverge, the existing empirical evidence is consistent with the information gain approach. Recommends that more empirical research is required to decide between these two…

Experimental data on human reasoning in hypothesis-testing tasks is reassessed in light of a Bayesian model of optimal data selection in inductive hypothesis testing. The rational analysis provided by the model suggests that reasoning in such tasks may be rational rather than subject to systematic bias. (SLD)

This article explores the potential of Bayesian inference as a theoretical framework for describing how people evaluate hypotheses. First, it identifies a set of logically possible forms of non-Bayesian behavior. Second, it reviews existing research in a variety of areas to see whether these possibilities are ever realized. (Author/BW)