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In this study, we propose a two-stage procedure for hypothesis testing, wherethe first stage is conventional hypothesis testing and the second is anequivalence testing procedure using an introduced Empirical Equivalence Bound.In 2016, the American Statistical Association released a policy statement onP-values to clarify the proper use and interpretation in response to thecriticism of reproducibility and replicability in scientific findings. A recentsolution to improve reproducibility and transparency in statistical hypothesistesting is to integrate P-values (or confidence intervals) with practical orscientific significance. Similar ideas have been proposed via the equivalencetest, where the goal is to infer equality under a presumption (null) ofinequality of parameters. However, in these testing procedures, the definitionof scientific significance/equivalence can be subjective. To circumvent thisdrawback, we introduce a B-value and the Empirical Equivalence Bound, which areboth estimated from the data. Performing a second-stage equivalence test, ourprocedure offers an opportunity to correct for false positive discoveries andimprove the reproducibility in findings across studies.

B-Value and Empirical Equivalence Bound: A New Procedure of Hypothesis Testing