Abstract Virtually all scientific outlets, including the most prestigious journals, have implemented strict rules of α and (1–β) control, supposed to quantify the probability of a significant result assuming H0 and H1, respectively. However, estimation of α and β rests on the untenable assumption that a systematic effect ΔY in the dependent variable cannot be brought about by any other causal influence than the influence ΔX stated in H1 and negated in H0. Yet, in a given study, empirical evidence on ΔY related to ΔX can always reflect extraneous causal influences, because no treatment or measurement tool affords a pure measure of X and Y, respectively. Consequently, α and β cannot quantify error probabilities in specific studies.

中文翻译：

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多因世界中难以捉摸的 Alpha 和 Beta 控制

摘要 几乎所有的科学媒体，包括最负盛名的期刊，都实施了严格的 α 和 (1-β) 控制规则，分别假设 H0 和 H1 来量化显着结果的概率。然而，α 和 β 的估计依赖于一个站不住脚的假设，即因变量中的系统效应 ΔY 不能由任何其他因果影响带来，而不是在 H1 中陈述并在 H0 中否定的影响 ΔX。然而，在给定的研究中，与 ΔX 相关的 ΔY 的经验证据总是可以反映无关的因果影响，因为没有治疗或测量工具分别提供 X 和 Y 的纯测量。因此，α 和 β 无法量化特定研究中的错误概率。