{"title":"On a generalizable approach for sample size determination in Bayesian t tests.","authors":"Tsz Keung Wong, Jorge N Tendeiro","doi":"10.3758/s13428-025-02654-x","DOIUrl":null,"url":null,"abstract":"<p><p>The Bayes factor is often proposed as a superior replacement to p values in testing null hypotheses for various reasons, with the availability of many user-friendly and easily accessible statistical software tools facilitating the use of Bayesian tests. Meanwhile, Bayes factor design analysis (BFDA), the counterpart of power analysis, is also proposed to ensure the maximum efficiency and informativeness of a study. Despite tools for conducting BFDA being limited and mostly relying on Monte Carlo methodology, methods based on root-finding algorithms have been recently developed (e.g., Pawel and Held, 2025), overcoming weaknesses of simulation approaches. This paper builds on these advancements by presenting a method generalizing the existing approach for conducting BFDA for sample size determination in t tests. The major advantage of the current method is that it does not assume normality of the effect size estimate, allowing more flexibility in the specification of the design and analysis priors. We developed and showcase a user-friendly Shiny app for facilitating the use of BFDA, illustrated with an empirical example. Furthermore, using our method, we explore the operating characteristics of the Bayes factors using various priors.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 5","pages":"130"},"PeriodicalIF":4.6000,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11958428/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavior Research Methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3758/s13428-025-02654-x","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
摘要
由于种种原因,贝叶斯因子经常被提议作为检验零假设的 p 值的优越替代品,许多用户友好、易于使用的统计软件工具的出现也为贝叶斯检验的使用提供了便利。同时,与功率分析相对应的贝叶斯因子设计分析(BFDA)也被提出来,以确保研究的最大效率和信息量。尽管进行贝叶斯因子设计分析的工具有限,而且大多依赖蒙特卡罗方法,但最近开发出了基于寻根算法的方法(如 Pawel 和 Held,2025 年),克服了模拟方法的弱点。本文在这些进展的基础上,提出了一种方法,对现有方法进行了概括,用于在 t 检验中确定样本量的 BFDA。当前方法的主要优点是不假定效应大小估计值的正态性,从而使设计和分析先验的规范更具灵活性。我们开发并展示了一个用户友好的 Shiny 应用程序,以方便使用 BFDA,并通过一个实证例子进行了说明。此外,利用我们的方法,我们探索了贝叶斯因子在不同先验条件下的运行特征。
On a generalizable approach for sample size determination in Bayesian t tests.
The Bayes factor is often proposed as a superior replacement to p values in testing null hypotheses for various reasons, with the availability of many user-friendly and easily accessible statistical software tools facilitating the use of Bayesian tests. Meanwhile, Bayes factor design analysis (BFDA), the counterpart of power analysis, is also proposed to ensure the maximum efficiency and informativeness of a study. Despite tools for conducting BFDA being limited and mostly relying on Monte Carlo methodology, methods based on root-finding algorithms have been recently developed (e.g., Pawel and Held, 2025), overcoming weaknesses of simulation approaches. This paper builds on these advancements by presenting a method generalizing the existing approach for conducting BFDA for sample size determination in t tests. The major advantage of the current method is that it does not assume normality of the effect size estimate, allowing more flexibility in the specification of the design and analysis priors. We developed and showcase a user-friendly Shiny app for facilitating the use of BFDA, illustrated with an empirical example. Furthermore, using our method, we explore the operating characteristics of the Bayes factors using various priors.
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.