Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology.
Perspect Psychol Sci. 2021 May;16(3):639-648. doi: 10.1177/1745691620958012. Epub 2021 Feb 9.
Because of the strong overreliance on values in the scientific literature, some researchers have argued that we need to move beyond values and embrace practical alternatives. When proposing alternatives to 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 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 values affect the quality of scientific research, preventing the misinterpretation of 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.
由于科学文献中对价值观的强烈过度依赖，一些研究人员认为，我们需要超越价值观，转而采用实用的替代方案。当统计学家提出价值观的替代方案时，他们往往会犯“统计学家的谬误”，即他们宣称哪种统计方法是研究人员真正“想知道”的。统计学家不应该告诉研究人员他们想知道什么，而应该教会他们可以提出哪些问题。在某些情况下，他们最感兴趣的问题的答案将是 值。只要对零假设检验的批评仍然存在，研究人员就一直建议在我们的统计工具包中纳入最小效应检验和等效性检验，这些检验有可能极大地改进研究人员提出的问题。如果有人认为价值观会影响科学研究的质量，那么通过开展更好的基于证据的教育和以用户为中心的统计软件来防止对 值的误解应该是当务之急。关于统计学家应该使用哪种统计方法的两极化讨论分散了我们对更重要问题的注意力，例如，当研究人员进行科学研究时，询问他们想知道什么。在我们能够改进我们的统计推断之前，我们需要改进我们的统计问题。
