Department of Mathematics, University of Siegen, Walter-Flex-Street 3, 57072, Siegen, Germany.
Behav Res Methods. 2022 Jun;54(3):1114-1130. doi: 10.3758/s13428-021-01613-6. Epub 2021 Sep 1.
Hypothesis testing is a central statistical method in psychology and the cognitive sciences. However, the problems of null hypothesis significance testing (NHST) and p values have been debated widely, but few attractive alternatives exist. This article introduces the fbst R package, which implements the Full Bayesian Significance Test (FBST) to test a sharp null hypothesis against its alternative via the e value. The statistical theory of the FBST has been introduced more than two decades ago and since then the FBST has shown to be a Bayesian alternative to NHST and p values with both theoretical and practical highly appealing properties. The algorithm provided in the fbst package is applicable to any Bayesian model as long as the posterior distribution can be obtained at least numerically. The core function of the package provides the Bayesian evidence against the null hypothesis, the e value. Additionally, p values based on asymptotic arguments can be computed and rich visualizations for communication and interpretation of the results can be produced. Three examples of frequently used statistical procedures in the cognitive sciences are given in this paper, which demonstrate how to apply the FBST in practice using the fbst package. Based on the success of the FBST in statistical science, the fbst package should be of interest to a broad range of researchers and hopefully will encourage researchers to consider the FBST as a possible alternative when conducting hypothesis tests of a sharp null hypothesis.
假设检验是心理学和认知科学中的一种核心统计方法。然而，关于零假设显著性检验（NHST）和 p 值的问题已经被广泛讨论，但很少有吸引人的替代方案存在。本文介绍了 fbst R 包，它实现了全贝叶斯显著性检验（FBST），通过 e 值来检验尖锐零假设与其替代假设。FBST 的统计理论早在二十多年前就已经提出，从那时起，FBST 已经被证明是 NHST 和 p 值的贝叶斯替代方案，具有理论和实践上都极具吸引力的特性。fbst 包中提供的算法适用于任何贝叶斯模型，只要能够至少通过数值方法获得后验分布。该包的核心功能提供了对零假设的贝叶斯证据，即 e 值。此外，还可以计算基于渐近论点的 p 值，并生成丰富的可视化效果，用于结果的交流和解释。本文给出了认知科学中三个常用统计过程的示例，演示了如何使用 fbst 包在实践中应用 FBST。基于 FBST 在统计学中的成功，fbst 包应该会引起广泛研究人员的兴趣，并希望鼓励研究人员在进行尖锐零假设的假设检验时，将 FBST 作为一种可能的替代方案来考虑。
