论文标题
完整的贝叶斯显着性测试和电子价值 - 认知科学中的基础，理论和应用
The Full Bayesian Significance Test and the e-value -- Foundations, theory and application in the cognitive sciences
论文作者
论文摘要
假设检验是心理学研究和认知科学中的一种核心统计方法。尽管无效假设意义检验（NHST）的问题已经广泛争议，但很少有吸引人的选择。在本文中，我们提供了一个有关贝叶斯显着性测试（FBST）和电子价值的教程，该教程是依靠P值的传统显着性测试的完全贝叶斯替代方案。 FBST是一种高级方法论程序，可以应用于多个领域。在本教程中，我们展示了两个在心理学研究中广泛使用统计方法的示例，如何在实践中使用FBST，为研究人员提供有关如何进行操作的明确指南，并提供ROD ROD来重现所有结果。 FBST是一种创新的方法，已清楚地证明其性能比频繁的显着性测试更好。但是，据我们所知，它尚未在心理学科学中使用，应该对心理学和认知科学领域的广泛研究人员产生广泛的兴趣。
Hypothesis testing is a central statistical method in psychological research and the cognitive sciences. While the problems of null hypothesis significance testing (NHST) have been debated widely, few attractive alternatives exist. In this paper, we provide a tutorial on the Full Bayesian Significance Test (FBST) and the e-value, which is a fully Bayesian alternative to traditional significance tests which rely on p-values. The FBST is an advanced methodological procedure which can be applied to several areas. In this tutorial, we showcase with two examples of widely used statistical methods in psychological research how the FBST can be used in practice, provide researchers with explicit guidelines on how to conduct it and make available R-code to reproduce all results. The FBST is an innovative method which has clearly demonstrated to perform better than frequentist significance testing. However, to our best knowledge, it has not been used so far in the psychological sciences and should be of wide interest to a broad range of researchers in psychology and the cognitive sciences.
