Three Recommendations for Improving the Use of p-Values
Three Recommendations for Improving the Use of p-Values
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DOI：
10.1080/00031305.2018.1543135
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发表时间：
2019-01-01
影响因子：
1.8
通讯作者：
Berger, James O.
中科院分区：
文献类型：
--
作者：
Benjamin, Daniel J.;Berger, James O.
研究人员通常使用p值来回答这个问题：相对于零假设，证据对备择假设的支持程度如何？p值本身并不能直接回答这个问题，并且经常被误解，导致夸大反对零假设的证据。然而，即使在“后p < 0.05时代”，p值也很有可能继续被广泛报道并用于评估证据的强度（如果没有其他原因，仅仅是统计软件的广泛可用性和使用，这些软件经常产生p值，从而隐含地倡导使用它们）。如果是这样，误解的可能性将持续存在。在这篇文章中，我们推荐了三种可以帮助研究人员更准确地解释p值的方法。这三种推荐做法中的每一种都涉及根据其相应的“贝叶斯因子界”来解释p值，贝叶斯因子界是相对于与观察数据一致的零假设而言，有利于备择假设的最大几率。贝叶斯因子界通常表明，给定的p值提供的反对零假设的证据比通常假设的证据更弱。因此，我们相信，我们的建议可以防止一些最有害的p值误解，在研究社区，深深依赖于“p < 0.05”，我们的建议将作为远离这种附件的最初步骤。我们强调，我们的建议仅仅是初步的、临时的步骤，还需要采取许多进一步的步骤才能达到最终目的：对统计证据进行全面的解释，完全符合阿萨关于统计显著性和p值的声明中规定的原则。
Researchers commonly use p-values to answer the question: How strongly does the evidence favor the alternative hypothesis relative to the null hypothesis? p-Values themselves do not directly answer this question and are often misinterpreted in ways that lead to overstating the evidence against the null hypothesis. Even in the "post p < 0.05 era," however, it is quite possible that p-values will continue to be widely reported and used to assess the strength of evidence (if for no other reason than the widespread availability and use of statistical software that routinely produces p-values and thereby implicitly advocates for their use). If so, the potential for misinterpretation will persist. In this article, we recommend three practices that would help researchers more accurately interpret p-values. Each of the three recommended practices involves interpreting p-values in light of their corresponding "Bayes factor bound," which is the largest odds in favor of the alternative hypothesis relative to the null hypothesis that is consistent with the observed data. The Bayes factor bound generally indicates that a given p-value provides weaker evidence against the null hypothesis than typically assumed. We therefore believe that our recommendations can guard against some of the most harmful p-value misinterpretations, In research communities that are deeply attached to reliance on "p < 0.05," our recommendations will serve as initial steps away from this attachment. We emphasize that our recommendations are intended merely as initial, temporary steps and that many further steps will need to be taken to reach the ultimate destination: a holistic interpretation of statistical evidence that fully conforms to the principles laid out in the ASA statement on statistical significance and p-values.