The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective

被引:717
作者
Kruschke, John K. [1 ]
Liddell, Torrin M. [1 ]
机构
[1] Indiana Univ, Bloomington, IN 47405 USA
关键词
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; Randomized controlled trial; Equivalence testing; CONFIDENCE-INTERVALS; NULL HYPOTHESIS; EQUIVALENCE; PSYCHOLOGY; INFERENCE; FAILURE;
D O I
10.3758/s13423-016-1221-4
中图分类号
B841 [心理学研究方法];
学科分类号
040201 ;
摘要
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. 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.
引用
收藏
页码:178 / 206
页数:29
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