Hypothesis evaluation from a Bayesian perspective.

1983-01-01articlePsychological Review539 citationsclosed

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Decision-Making and Behavioral Economics Statistical Methods and Bayesian Inference Bayesian Modeling and Causal Inference

Abstract

Bayesian inference provides a general framework for evaluating hypotheses. It is a normative method in the sense of prescribing how hypotheses should be evaluated. However, it may also be used descriptively by characterizing people's actual hypothesis-evaluation behavior in terms of its consistency with or departures from the model. Such a characterization may facilitate the development of psychological accounts of how that behavior is produced. 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 nonBayesian behavior. Second, it reviews existing research in a variety of areas to see whether these possibilities are ever realized. The analysis shows that in some situations several apparently distinct phenomena are usefully viewed as special cases of the same kind of behavior, whereas in other situations previous investigations have conferred a common label (e.g., confirmation bias) to several distinct phenomena. It also calls into question a number of attributions of judgmental bias, suggesting that in some cases the bias is different than what has previously been claimed, whereas in others there may be no bias at all.

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