Martínez-Camblor P, Pérez-Fernández S, Díaz-Coto S
Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA.
Universidad de Oviedo, Asturies, Spain.
J Appl Stat. 2019 Oct 23;47(9):1529-1542. doi: 10.1080/02664763.2019.1682128. eCollection 2020.
Modern science frequently involves the analysis of large amount of quantitative information and the simultaneous testing of thousands or even hundreds of thousands null hypotheses. In this context, sometimes, naive deductions derived from the statistical reports substitute the rational thinking. The is a direct consequence of the misleading statistical conclusions. In this paper, the authors revisit some of the controversies on the implications derived from the statistical hypothesis testing. They focus on the role of the -value on the massive multitesting problem and the loss of its standard probabilistic interpretation. The analogy between the hypothesis tests and the usual diagnostic process (both involve a decision-making) is used to point out some limitations in the probabilistic -value interpretation and to introduce the receiver-operating characteristic, ROC, curve as a useful tool in the large-scale multitesting context. The analysis of the well-known Hedenfalk data illustrates the problem.
现代科学常常涉及对大量定量信息的分析以及对成千上万甚至数十万原假设的同时检验。在这种情况下,有时从统计报告中得出的天真推断会取代理性思考。这是误导性统计结论的直接后果。在本文中,作者重新审视了一些关于统计假设检验所产生影响的争议。他们关注P值在大规模多重检验问题中的作用以及其标准概率解释的丧失。假设检验与通常的诊断过程(两者都涉及决策)之间的类比被用来指出概率P值解释中的一些局限性,并引入接收者操作特征(ROC)曲线作为大规模多重检验背景下的一个有用工具。对著名的赫登法尔克数据的分析说明了这个问题。