Dienes, Zoltan Testing theories with Bayes factors. In: Nichols, Austin Lee and Edlund, John E (eds.) Cambridge handbook of research methods and statistics for the social and behavioral sciences. Cambridge University Press. (Accepted)

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Abstract

Bayes factors, a measure of evidence for one model versus another, are a useful tool in the behavioral and social sciences, partly because they can provide evidence for no effect relative to the sort of effect expected. By contrast, a non-significant result does not provide evidence for the H0 tested. If non-significance does not in itself count against any theory predicting an effect, how could a theory fail a test? Bayes factors provide a measure of evidence from first principles. A severe test is one that is likely to obtain evidence against a theory if it were false — to obtain an extreme Bayes factor against the theory. Bayes factors show why cherry picking degrades evidence, how to deal with multiple testing, and how optional stopping is consistent with severe testing. Further, informed Bayes factors can be used to link theory tightly to how that theory is tested, so that the measured evidence does relate to the theory.

Item Type:	Book Section
Schools and Departments:	School of Psychology > Psychology
SWORD Depositor:	Mx Elements Account
Depositing User:	Mx Elements Account
Date Deposited:	21 Apr 2022 06:26
Last Modified:	21 Apr 2022 06:26
URI:	http://sro.sussex.ac.uk/id/eprint/105326

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