Rothman K J
Epidemiology. 1990 Jan;1(1):43-6.
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.
建议在大量数据中进行多重比较时进行调整，以避免轻易拒绝原假设。不幸的是，降低零关联的I型错误会增加非零关联的II型错误。主张对多重比较进行常规调整的理论基础是“普遍原假设”，即“偶然性”是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，实证研究认为自然遵循可以通过观察来研究的规律。不进行多重比较调整的策略更可取，因为当所评估的数据不是随机数而是对自然的实际观察时，它将导致更少的解释错误。此外，科学家不应如此不愿意探索可能被证明是错误的线索，以至于因错过可能重要的发现而自我惩罚。
