Scholarlink

<p>The Bayes factor is often proposed as a superior replacement to <i>p</i>&#xa0;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, <CitationRef CitationID="CR26">2025</CitationRef>), 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 <i>t</i> 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>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

On a generalizable approach for sample size determination in Bayesian t tests

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月31日

查看全文
  • Tsz Keung Wong,
  • Jorge N. Tendeiro

摘要

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.