The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective
The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective
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DOI：
10.3758/s13423-016-1221-4
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发表时间：
2018-02-01
影响因子：
3.5
通讯作者：
Liddell, Torrin M.
中科院分区：
文献类型：
--
作者：
Kruschke, John K.;Liddell, Torrin M.
在数据分析的实践中，一方面是假设检验，另一方面是带有量化不确定性的估计之间的概念上的区别。在心理学的常客中，将重点从假设检验转移到估计被称为“新统计”(Cumming 2014)。第二个概念上的区别是频数方法和贝叶斯方法之间的区别。我们在这篇文章中的主要目的是解释贝叶斯方法如何更好地实现新统计学的目标。本文回顾了假设检验的频度和贝叶斯方法，以及用置信度或可信区间估计的方法。文章还介绍了贝叶斯方法的荟萃分析，随机对照试验，和力量分析。
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.