No adjustments are needed for multiple comparisons.
No adjustments are needed for multiple comparisons.
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DOI：
10.1097/00001648-199001000-00010
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发表时间：
1990-01-01
期刊：
影响因子：
--
通讯作者：
Rothman, K J
中科院分区：
文献类型：
--
作者：
Rothman, K J
建议在大数据体中进行多次比较时进行调整，以避免过于轻易地拒绝零假设。不幸的是，减少空关联的类型I错误会增加非空关联的类型II错误。提倡对多重比较进行常规调整的理论基础是“普遍零假设”，即“偶然性”是对观察到的现象的一阶解释。这一假设破坏了实证研究的基本前提，即自然遵循可以通过观察来研究的规律。不进行多次比较调整的政策是可取的，因为当评估的数据不是随机数而是对自然的实际观察时，这将导致更少的解释错误。此外，科学家不应该太不情愿去探索那些可能被证明是错误的线索，而以错过可能重要的发现来惩罚自己。
Adjustments for making multiple comparisons in large bodies of data are recommended to avoid rejecting the null hypothesis too readily. Unfortunately, reducing the type I error for null associations increases the type II error for those associations that are not null. The theoretical basis for advocating a routine adjustment for multiple comparisons is the "universal null hypothesis" that "chance" serves as the first-order explanation for observed phenomena. This hypothesis undermines the basic premises of empirical research, which holds that nature follows regular laws that may be studied through observations. A policy of not making adjustments for multiple comparisons is preferable because it will lead to fewer errors of interpretation when the data under evaluation are not random numbers but actual observations on nature. Furthermore, scientists should not be so reluctant to explore leads that may turn out to be wrong that they penalize themselves by missing possibly important findings.