B-Value and Empirical Equivalence Bound: A New Procedure of Hypothesis Testing
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Publication:141077
DOI10.48550/ARXIV.1912.13084arXiv1912.13084MaRDI QIDQ141077
Author name not available (Why is that?)
Publication date: 30 December 2019
Abstract: In this study, we propose a two-stage procedure for hypothesis testing, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using an introduced Empirical Equivalence Bound. In 2016, the American Statistical Association released a policy statement on P-values to clarify the proper use and interpretation in response to the criticism of reproducibility and replicability in scientific findings. A recent solution to improve reproducibility and transparency in statistical hypothesis testing is to integrate P-values (or confidence intervals) with practical or scientific significance. Similar ideas have been proposed via the equivalence test, where the goal is to infer equality under a presumption (null) of inequality of parameters. However, in these testing procedures, the definition of scientific significance/equivalence can be subjective. To circumvent this drawback, we introduce a B-value and the Empirical Equivalence Bound, which are both estimated from the data. Performing a second-stage equivalence test, our procedure offers an opportunity to correct for false positive discoveries and improve the reproducibility in findings across studies.
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