During multiple testing, researchers often adjust their alpha level to control the familywise error rate for a statistical inference about a joint union alternative hypothesis (e.g., "H1 or H2"). However, in some cases, they do not make this inference and instead make separate inferences about each of the individual hypotheses that comprise the joint hypothesis (e.g., H1 and H2). For example, a researcher might use a Bonferroni correction to adjust their alpha level from the conventional level of 0.050 to 0.025 when testing H1 and H2, find a significant result for H1 (p < 0.025) and not for H2 (p > .0.025), and so claim support for H1 and not for H2. However, these separate individual inferences do not require an alpha adjustment. Only a statistical inference about the union alternative hypothesis "H1 or H2" requires an alpha adjustment because it is based on "at least one" significant result among the two tests, and so it depends on the familywise error rate. When a researcher corrects their alpha level during multiple testing but does not make an inference about the union alternative hypothesis, their correction is redundant. In the present article, I discuss this redundant correction problem, including its associated loss of statistical power and its potential causes vis-\`a-vis error rate confusions and the alpha adjustment ritual. I also provide three illustrations of redundant corrections from recent psychology studies. I conclude that redundant corrections represent a symptom of statisticism, and I call for a more nuanced and context-specific approach to multiple testing corrections.

翻译：在多重检验中，研究者常调整显著性水平α以控制族系错误率，从而对联合备择假设（如“H1或H2”）进行统计推断。然而在某些情况下，他们并未进行此类推断，而是对构成联合假设的各个假设（如H1和H2）分别进行单独推断。例如，研究者可能采用Bonferroni校正将传统显著性水平0.050调整为0.025来检验H1和H2，若发现H1显著（p < 0.025）而H2不显著（p > 0.025），则声称支持H1否定H2。但这类独立的单独推断并不需要调整α水平。仅当对联合备择假设“H1或H2”进行统计推断时，才需要调整α水平——因为这类推断依赖于“两个检验中至少一个显著”的结论，故与族系错误率相关。当研究者调整α水平却未对联合备择假设做出推断时，其校正行为即为冗余。本文讨论这一冗余校正问题，包括伴随的统计功效损失、误差率混淆及α调整惯例等潜在成因，并展示近期三项心理学研究中的冗余校正实例。结论认为冗余校正是统计主义的表现，本文呼吁对多重检验校正采取更细致且符合具体情境的应对策略。