Obtaining Evidence for No Effect
Obtaining Evidence for No Effect
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DOI：
10.1525/collabra.28202
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发表时间：
2021-09-30
影响因子：
2.5
通讯作者：
Dienes, Zoltan
中科院分区：
文献类型：
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作者：
Dienes, Zoltan
要获得某个东西不存在的证据，需要知道它如果存在的话会有多大。因此，测试一个预测效应的理论需要指定与理论一致的效应量范围，以便知道证据何时对理论不利。实际上，必须为功效计算、等效性检验和贝叶斯因子指定理论上相关的效应量，以便推断统计检验理论。功效的显著相关效应量，或等效性检验的等效区域，或贝叶斯因子的比例因子，对于许多期刊格式（如注册报告）是必要的，并且对于所有使用假设检验的文章都是必要的。然而，关于如何解决这一问题，几乎没有系统的建议。本文提供了一些原则和实际的建议，为指定理论上相关的效应量的假设检验。
Obtaining evidence that something does not exist requires knowing how big it would be were it to exist. Testing a theory that predicts an effect thus entails specifying the range of effect sizes consistent with the theory, in order to know when the evidence counts against the theory. Indeed, a theoretically relevant effect size must be specified for power calculations, equivalence testing, and Bayes factors in order that the inferential statistics test the theory. Specifying relevant effect sizes for power, or the equivalence region for equivalence testing, or the scale factor for Bayes factors, is necessary for many journal formats, such as registered reports, and should be necessary for all articles that use hypothesis testing. Yet there is little systematic advice on how to approach this problem. This article offers some principles and practical advice for specifying theoretically relevant effect sizes for hypothesis testing.