Jorge N. Tendeiro, Rink Hoekstra, Tsz Keung Wong, Henk A. L. Kiers
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引用次数: 0
摘要
大多数研究人员在本科学习期间都接受过频繁统计的正规培训。特别是,假设检验通常植根于零假设显著性检验范式及其 p 值。现在,零假设贝叶斯检验及其所谓的贝叶斯因子正变得越来越流行。尽管贝叶斯因子经常被介绍为 p 值的贝叶斯对应物,但其计算、使用和解释与 p 值截然不同。现在有证据证实,贝叶斯因子在应用研究中的应用并不完善。为了改善目前的现状,我们创建了一个名为贝叶斯因子的 Shiny/R 应用程序,它提供了一个学习贝叶斯因子所有基础知识的动态教程。在本文中,我们将解释该应用程序的工作原理,并就如何在课堂或自学环境中使用该应用程序提出建议。该应用程序可在 https://statsedge.org/shiny/LearnBF/ 免费获取。
Most researchers receive formal training in frequentist statistics during their undergraduate studies. In particular, hypothesis testing is usually rooted on the null hypothesis significance testing paradigm and its p‐value. Null hypothesis Bayesian testing and its so‐called Bayes factor are now becoming increasingly popular. Although the Bayes factor is often introduced as being the Bayesian counterpart to the p‐value, its computation, use, and interpretation are quite distinct from the p‐value. There is now evidence confirming that the application of the Bayes factor in applied research is ill‐devised. To improve the current status quo, we have created a Shiny/R app called the Bayes factor, which offers a dynamic tutorial for learning all the basics about the Bayes factor. In this paper, we explain how the app works and we offer suggestions on how to use it in class or self‐study settings. The app is freely available at https://statsedge.org/shiny/LearnBF/.