Bayesian alternatives for common null-hypothesis significance tests in psychiatry: A non-technical guide using JASP

This article has now been published in BMC Psychiatry https://doi.org/10.1186/s12888-018-1761-4

Background: Despite its popularity as an inferential framework, classical null hypothesis significance testing (NHST) has several restrictions. Bayesian analysis can be used to complement NHST, however, this approach has been underutilized largely due to a dearth of accessible software options. JASP is a recently developed open-source statistical package that facilitates both Bayesian and NHST analysis using a graphical interface. This article provides an applied introduction to Bayesian inference with Bayes factors using JASP.

Methods: We use JASP to compare and contrast Bayesian alternatives for several common classical null hypothesis significance tests: correlations, frequency distributions, t-tests, ANCOVAs, and ANOVAs. These examples are also used to illustrate the strengths and limitations of both NHST and Bayesian hypothesis testing.

Results: A comparison of NHST and Bayesian inferential frameworks demonstrates that Bayes factors can complement p-values by providing additional information for hypothesis testing. Namely, Bayes factors can quantify relative evidence for both alternative and null hypotheses. Moreover, the magnitude of this evidence can be presented as an easy-to-interpret odds ratio.

Conclusions: While Bayesian analysis is by no means a new method, this type of statistical inference has been largely inaccessible for most psychiatry researchers. JASP provides a straightforward means of performing reproducible Bayesian hypothesis tests using a graphical “point and click” environment that will be familiar to researchers conversant with other graphical statistical packages, such as SPSS.

本文已发表于《BMC精神病》期刊，https://doi.org/10.1186/s12888-018-1761-4

背景：尽管经典的无假设检验显著性测试（NHST）作为一种推理框架广受欢迎，但它存在诸多限制。贝叶斯分析可以用于补充NHST，然而，由于缺乏易得的软件选项，这种方法在很大程度上未被充分利用。JASP是一个最近开发的开源统计软件包，它通过图形界面促进了贝叶斯和NHST分析。本文提供了使用JASP进行贝叶斯推断的实证介绍，并运用贝叶斯因子。

方法：我们使用JASP比较和对比了贝叶斯方法在几个常见的经典无假设检验显著性测试中的应用：相关性、频数分布、t检验、协方差分析（ANCOVA）和方差分析（ANOVA）。这些示例还用于阐述NHST和贝叶斯假设测试的优缺点。

结果：NHST和贝叶斯推断框架的比较表明，贝叶斯因子可以通过提供额外的假设检验信息来补充p值。具体而言，贝叶斯因子可以量化对备择假设和零假设的相对证据。此外，这种证据的强度可以以易于理解的几率比的形式呈现。

结论：尽管贝叶斯分析并非一种新方法，但这种类型的统计推断对于大多数精神病学研究人员来说在很大程度上是难以接触到的。JASP提供了一个简单直观的方法，通过图形的“点选”环境执行可重复的贝叶斯假设测试，这对于熟悉其他图形统计软件包（如SPSS）的研究人员来说将是熟悉的。