A Good check on the Bayes factor.

IF 4.6 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Behavior Research Methods Pub Date : 2024-12-01 Epub Date: 2024-09-04 DOI:10.3758/s13428-024-02491-4
Nikola Sekulovski, Maarten Marsman, Eric-Jan Wagenmakers
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Abstract

Bayes factor hypothesis testing provides a powerful framework for assessing the evidence in favor of competing hypotheses. To obtain Bayes factors, statisticians often require advanced, non-standard tools, making it important to confirm that the methodology is computationally sound. This paper seeks to validate Bayes factor calculations by applying two theorems attributed to Alan Turing and Jack Good. The procedure entails simulating data sets under two hypotheses, calculating Bayes factors, and assessing whether their expected values align with theoretical expectations. We illustrate this method with an ANOVA example and a network psychometrics application, demonstrating its efficacy in detecting calculation errors and confirming the computational correctness of the Bayes factor results. This structured validation approach aims to provide researchers with a tool to enhance the credibility of Bayes factor hypothesis testing, fostering more robust and trustworthy scientific inferences.

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贝叶斯系数的良好检验
贝叶斯系数假设检验提供了一个强大的框架,用于评估有利于相互竞争的假设的证据。为了获得贝叶斯因子,统计学家通常需要使用先进的非标准工具,因此确认该方法在计算上是否合理非常重要。本文试图通过应用艾伦-图灵和杰克-古德的两个定理来验证贝叶斯系数的计算。这一过程需要模拟两个假设下的数据集,计算贝叶斯系数,并评估其预期值是否与理论预期一致。我们用一个方差分析实例和一个网络心理测量应用来说明这种方法,展示了它在检测计算错误和确认贝叶斯因子结果的计算正确性方面的功效。这种结构化验证方法旨在为研究人员提供一种工具,以提高贝叶斯因子假设检验的可信度,促进更稳健、更可信的科学推论。
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来源期刊
CiteScore
10.30
自引率
9.30%
发文量
266
期刊介绍: Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.
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