Reconnecting p-Value and Posterior Probability Under One- and Two-Sided Tests

DOI10.1080/00031305.2020.1717621OpenAlexW2891886352WikidataQ126317879 ScholiaQ126317879MaRDI QIDQ5056974

Guosheng Yin, Haolun Shi

Publication date: 14 December 2022

Published in: The American Statistician (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00031305.2020.1717621

zbMATH Keywords

hypothesis testing\(p\)-valueclinical trialposterior probabilityone-sided testtwo-sided test

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (5)

Publication Policies for Replicable Research and the Community-Wide False Discovery Rate ⋮ Null Hypothesis Significance Testing Interpreted and Calibrated by Estimating Probabilities of Sign Errors: A Bayes-Frequentist Continuum ⋮ The \(p\)-value interpreted as the posterior probability of explaining the data: applications to multiple testing and to restricted parameter spaces ⋮ Fiducialize statistical significance: transformingp-values into conservative posterior probabilities and Bayes factors ⋮ Interval estimation, point estimation, and null hypothesis significance testing calibrated by an estimated posterior probability of the null hypothesis

Cites Work

• Unnamed Item
• Bayesianly justifiable and relevant frequency calculations for the applied statistician
• Could Fisher, Jeffreys and Neyman have agreed on testing? (With comments and a rejoinder).
• Testing precise hypotheses. With comments and a rejoinder by the authors
• The interplay of Bayesian and frequentist analysis
• Asymptotic normality with small relative errors of posterior probabilities of half-spaces
• Revised standards for statistical evidence
• A STATISTICAL PARADOX
• Reconciling Bayesian and Frequentist Evidence in the One-Sided Testing Problem
• Testing a Point Null Hypothesis: The Irreconcilability of P Values and Evidence
• The Behavior of the P-Value When the Alternative Hypothesis is True
• The Effect of Sample Size on the Meaning of Significance Tests
• Calibration ofρValues for Testing Precise Null Hypotheses
• Testing Statistical Hypotheses
• The p-Value Requires Context, Not a Threshold
• Three Recommendations for Improving the Use of p-Values
• Moving Towards the Post p < 0.05 Era via the Analysis of Credibility
• Abandon Statistical Significance
• Predictive Inference and Scientific Reproducibility
• Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing
• The ASA Statement on p-Values: Context, Process, and Purpose

This page was built for publication: Reconnecting p-Value and Posterior Probability Under One- and Two-Sided Tests