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- W2891886352 abstract "As a convention, p-value is often computed in frequentist hypothesis testing and compared with the nominal significance level of 0.05 to determine whether or not to reject the null hypothesis. The smaller the p-value, the more significant the statistical test. Under noninformative prior distributions, we establish the equivalence relationship between the p-value and Bayesian posterior probability of the null hypothesis for one-sided tests and, more importantly, the equivalence between the p-value and a transformation of posterior probabilities of the hypotheses for two-sided tests. For two-sided hypothesis tests with a point null, we recast the problem as a combination of two one-sided hypotheses along the opposite directions and establish the notion of a “two-sided posterior probability,” which reconnects with the (two-sided) p-value. In contrast to the common belief, such an equivalence relationship renders p-value an explicit interpretation of how strong the data support the null. Extensive simulation studies are conducted to demonstrate the equivalence relationship between the p-value and Bayesian posterior probability. Contrary to broad criticisms on the use of p-value in evidence-based studies, we justify its utility and reclaim its importance from the Bayesian perspective." @default.
- W2891886352 created "2018-09-27" @default.
- W2891886352 creator A5066373758 @default.
- W2891886352 creator A5080151722 @default.
- W2891886352 date "2020-02-25" @default.
- W2891886352 modified "2023-10-08" @default.
- W2891886352 title "Reconnecting <i>p</i>-Value and Posterior Probability Under One- and Two-Sided Tests" @default.
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- W2891886352 doi "https://doi.org/10.1080/00031305.2020.1717621" @default.
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