In this study, a two-stage procedure for hypothesis testing is proposed, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using an introduced Empirical Equivalence Bound. The use of P-values in hypothesis testing has a long history, dating back to 1925 when R.A. Fisher introduced and promoted it for rejecting a null hypothesis when small. Since then, there are ongoing discussions about how to correctly use and interpret them. Common misunderstanding and misuse of P-values are likely partially responsible for general confusion and mistrust of empirical findings. 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, the definition of scientific significance/equivalence can sometimes be ill-justified and subjective. To circumvent this drawback, two concepts, B-value and Empirical Equivalence Bound, are introduced, which are both derived from the data. Performing a second-stage equivalence test, it offers an opportunity to examine whether the significant result in the conventional hypothesis testing is a false positive discovery. This new two-stage testing procedure may then help improve the reproducibility of findings across studies.