A Test by Any Other Name:PValues, Bayes Factors, and Statistical Inference

2016/01/02 by Hal S. Stern · 1 citation
Decision Sciences · Computer Science · Mathematics · Psychology · #Meta-analysis and systematic reviews #Data Analysis with R #Statistical Methods in Clinical Trials #Statistical inference #Null hypothesis #Bayes' theorem #Statistical hypothesis testing #Inference #Fiducial inference #Bayes factor #Frequentist inference #Bayesian inference #Bayesian statistics #Econometrics #Bayesian probability #Statistics #Frequentist probability #Scrutiny #Alternative hypothesis #Computer science #Psychology #Artificial intelligence #Mathematics

paper · doi:10.1080/00273171.2015.1099032

openalex publication_date 2016/01/02 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/01

Abstract

Procedures used for statistical inference are receiving increased scrutiny as the scientific community studies the factors associated with insuring reproducible research. This note addresses recent negative attention directed at p values, the relationship of confidence intervals and tests, and the role of Bayesian inference and Bayes factors, with an eye toward better understanding these different strategies for statistical inference. We argue that researchers and data analysts too often resort to binary decisions (e.g., whether to reject or accept the null hypothesis) in settings where this may not be required.

Citations

• The earth is round (p < .05).
• Statistical methods in psychology journals: Guidelines and explanations.
• Feeling the future: Experimental evidence for anomalous retroactive influences on cognition and affect.
• The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant
• Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy
• The Counternull Value of an Effect Size: A New Statistic
• False-Positive Psychology
• Bayes Factors
• Understanding The New Statistics

Cited by

• P-value [wikipedia]