This article provides guidance on conducting Bayesian hypothesis tests and reporting them, in order to aid in their understanding. To use and report a Bayesian hypothesis test, predicted effect sizes must be specified. The paper will provide guidance in specifying effect sizes of interest (which also will be of relevance to those using frequentist statistics). First, if a minimally interesting effect size can be specified, a null interval is defined as the effects smaller in magnitude than the

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... inimally interesting effect. Then the proportion of the posterior distribution that falls in the null interval indicates the plausibility of the null interval hypothesis. Second, if a rough scale of effect can be determined, a Bayes factor can indicate evidence for a model representing that scale of effect versus a model of H0. Both methods allow data to count against a theory that predicts a difference. By contrast, non-significance does not count against such a theory.