Researchers attempt to minimize Type-I errors (concluding there is a relationship between variables, when there in fact, isn't one) in their experiments by exerting control over the p-value thresholds or alpha level. If a statistical test is conducted only once in a study, it is indeed possible for the researcher to maintain control, so that the likelihood of a Type-I error is equal to or less than the significance (p-value) level. When making multiple comparisons in a study, however, the likelihood of making a Type-I error can dramatically increase. When conducting multiple comparisons, researchers frequently attempt to control for the increased risk of Type-I errors by making adjustments to their alpha level or significance threshold level. The Bonferroni adjustment is the most common of these types of adjustment. However, these, often rigid adjustments, are not without risk and are often applied arbitrarily. The objective of this review is to provide a balanced commentary on the advantages and disadvantages of making adjustments when undertaking multiple comparisons. A summary discussion of familiar- and experiment-wise error is also presented. Lastly, advice on when researchers should consider making adjustments in p-value thresholds and when they should be avoided, is provided.

研究人员试图通过对 p 值阈值或 alpha 水平施加控制，在他们的实验中尽量减少 I 型错误（得出变量之间存在关系，但实际上并非如此）。如果在一项研究中仅进行一次统计检验，研究人员确实有可能保持控制，从而使 I 类错误的可能性等于或小于显着性（p 值）水平。然而，在一项研究中进行多重比较时，犯第一类错误的可能性会大大增加。在进行多重比较时，研究人员经常尝试通过调整其 alpha 水平或显着性阈值水平来控制 I 型错误风险的增加。Bonferroni 调整是这些调整类型中最常见的。然而，这些，通常是严格的调整，并非没有风险，并且经常被任意应用。本次审查的目的是对进行多重比较时进行调整的利弊提供平衡的评论。还提供了对熟悉和实验错误的总结讨论。最后，提供了有关研究人员何时应考虑调整 p 值阈值以及何时应避免调整的建议。