A tutorial on a practical Bayesian alternative to null-hypothesis significance testing
A tutorial on a practical Bayesian alternative to null-hypothesis significance testing
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DOI：
10.3758/s13428-010-0049-5
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发表时间：
2011-09-01
影响因子：
5.4
通讯作者：
Masson, Michael E. J.
中科院分区：
文献类型：
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作者：
Masson, Michael E. J.
尽管存在严重的缺点，零假设显着性检验仍然是认知科学中的标准推理工具。其中最主要的事实是，所得到的概率值并没有告诉研究人员他或她通常想知道的信息：在给定所获得的数据的情况下，假设的可能性有多大？受 Wagenmakers 的发展启发（Psychonomic Bulletin & Review, 14, 779-804, 2007），我提供了一个关于贝叶斯模型选择方法的教程，该方法只需要对标准方差分析生成的平方和值进行简单的转换。这种方法生成了关于哪种模型（例如，效果不存在[原假设]与效果存在[替代假设]）更能得到数据支持的分级证据。这种方法还消除了永远不要谈论接受原假设的警告。用于计算贝叶斯分析的 Excel 工作表作为补充材料提供。
Null-hypothesis significance testing remains the standard inferential tool in cognitive science despite its serious disadvantages. Primary among these is the fact that the resulting probability value does not tell the researcher what he or she usually wants to know: How probable is a hypothesis, given the obtained data? Inspired by developments presented by Wagenmakers (Psychonomic Bulletin & Review, 14, 779-804, 2007), I provide a tutorial on a Bayesian model selection approach that requires only a simple transformation of sum-of-squares values generated by the standard analysis of variance. This approach generates a graded level of evidence regarding which model (e. g., effect absent [null hypothesis] vs. effect present [alternative hypothesis]) is more strongly supported by the data. This method also obviates admonitions never to speak of accepting the null hypothesis. An Excel worksheet for computing the Bayesian analysis is provided as supplemental material.