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

TL;DR

This paper introduces a two-stage hypothesis testing procedure that combines traditional testing with an empirical equivalence bound to enhance reproducibility and reduce false positives in scientific research.

Contribution

It proposes a novel two-stage method using B-value and Empirical Equivalence Bound estimated from data, improving reproducibility and objectivity in equivalence testing.

Findings

Reduces false positive discoveries

Improves reproducibility across studies

Provides data-driven equivalence bounds

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.