Because of the strong overreliance on p values in the scientific literature, some researchers have argued that we need to move beyond p values and embrace practical alternatives. When proposing alternatives to p values statisticians often commit the “statistician’s fallacy,” whereby they declare which statistic researchers really “want to know.” Instead of telling researchers what they want to know, statisticians should teach researchers which questions they can ask. In some situations, the answer to the question they are most interested in will be the p value. As long as null-hypothesis tests have been criticized, researchers have suggested including minimum-effect tests and equivalence tests in our statistical toolbox, and these tests have the potential to greatly improve the questions researchers ask. If anyone believes p values affect the quality of scientific research, preventing the misinterpretation of p values by developing better evidence-based education and user-centered statistical software should be a top priority. Polarized discussions about which statistic scientists should use has distracted us from examining more important questions, such as asking researchers what they want to know when they conduct scientific research. Before we can improve our statistical inferences, we need to improve our statistical questions.

由于科学文献中对p值的过度依赖，一些研究人员认为我们需要超越p值并接受实用的替代方案。在提出p值的替代方案时，统计学家经常犯“统计学家的谬误”，即他们宣称统计研究人员真正“想知道”哪些。统计学家不应告诉研究人员他们想知道什么，而应该教研究人员他们可以问哪些问题。在某些情况下，他们最感兴趣的问题的答案将是p价值。只要原假设检验受到批评，研究人员就建议在我们的统计工具箱中加入最小效应检验和等效检验，这些检验有可能极大地改善研究人员提出的问题。如果有人认为p值会影响科学研究的质量，那么通过开发更好的循证教育和以用户为中心的统计软件来防止对p值的误解应该是重中之重。关于科学家应该使用哪些统计数据的两极分化讨论分散了我们研究更重要问题的注意力，例如询问研究人员在进行科学研究时他们想知道什么。在我们改进统计推断之前，我们需要改进我们的统计问题。