Publisher's Synopsis
Excerpt from A Fortiori Bayesian Inference in Psychological Research
Bayes Law, or the law of conditional probability, provides a natural inference framework for one who views hypothesis testing as parameter estimation. Heretofore a major difficul ty in applying Bayesian ideas to psychological contexts has been the specification of an objective or public prior. This paper proposes a rule for selecting a prior hypothesis which is both unambiguous and hostile to the research hypothesis: choose the prior so that 1) its expectation is the conventional null value and 2) it has maximum probability of producing the obser ved data. The rule is employed to develop a complete set of tests for nominal data, and both a one-sample and a two-sample.
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