The Bayesian New Statistics: Hypothesis Testing, Estimation, Meta-Analysis, and Power Analysis from a Bayesian Perspective

28 Pages Posted: 15 May 2015 Last revised: 15 Dec 2016

John K. Kruschke

Indiana University

Torrin M. Liddell

Indiana University Bloomington

Date Written: November 15, 2016

Abstract

In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty, on the other hand. Among frequentists in psychology a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming, 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

Keywords: Null hypothesis significance testing, Bayesian inference, Bayes factor, confidence interval, credible interval, highest density interval, region of practical equivalence, meta-analysis, power analysis, effect size, random control trial

Suggested Citation

Kruschke, John K. and Liddell, Torrin M., The Bayesian New Statistics: Hypothesis Testing, Estimation, Meta-Analysis, and Power Analysis from a Bayesian Perspective (November 15, 2016). Available at SSRN: https://ssrn.com/abstract=2606016 or http://dx.doi.org/10.2139/ssrn.2606016

John K. Kruschke (Contact Author)

Indiana University ( email )

Dept. of Psychological and Brain Sciences
1101 E. 10th St.
Bloomington, IN 47405
United States

HOME PAGE: http://sites.google.com/site/doingbayesiandataanalysis/about-the-author

Torrin M. Liddell

Indiana University Bloomington ( email )

100 South Indiana Ave.
Bloomington, IN 47405
United States

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