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Abstract
In the field of cognitive psychology, the p-value hypothesis test has established
a stranglehold on statistical reporting. This is unfortunate, as the p-value provides
at best a rough estimate of the evidence that the data provide for the presence of
an experimental effect. An alternative and arguably more appropriate measure of evidence
is conveyed by a Bayesian hypothesis test, which prefers the model with the highest
average likelihood. One of the main problems with this Bayesian hypothesis test, however,
is that it often requires relatively sophisticated numerical methods for its computation.
Here we draw attention to the Savage-Dickey density ratio method, a method that can
be used to compute the result of a Bayesian hypothesis test for nested models and
under certain plausible restrictions on the parameter priors. Practical examples demonstrate
the method's validity, generality, and flexibility.
Copyright 2009 Elsevier Inc. All rights reserved.