There is growing frustration with the concept of the p-value. Besides having an ambiguous interpretation, the p-value can be made as small as desired by increasing the sample size, n. The p-value is outdated and does not make sense with big data: Everything becomes statistically significant. The root of the problem with the p-value is in the mean comparison. We argue that statistical uncertainty should be measured on the individual, not the group, level. Consequently, standard deviation (SD), not standard error (SE), error bars should be used to graphically present the data on two groups. We introduce a new measure based on the discrimination of individuals/objects from two groups, and call it the D-value. The D-value can be viewed as the n-of-1 p-value because it is computed in the same way as p while letting n equal 1. We show how the D-value is related to discrimination probability and the area above the receiver operating characteristic (ROC) curve. The D-value has a clear interpretation as the proportion of patients who get worse after the treatment, and as such facilitates to weigh up the likelihood of events under different scenarios. [Received January 2015. Revised June 2015.]

中文翻译：

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你买不到的 p 值


人们对 p 值的概念越来越不满。除了具有不明确的解释之外，还可以通过增加样本大小 n 将 p 值设置为尽可能小。 p 值已经过时，并且对于大数据没有意义：一切都变得具有统计显着性。 p 值问题的根源在于均值比较。我们认为统计不确定性应该在个人层面而不是群体层面进行衡量。因此，应使用标准差 (SD)，而不是标准误差 (SE) 误差线来以图形方式呈现两组数据。我们引入了一种基于对来自两个群体的个体/物体的区分的新度量，并将其称为D值。 D 值可以被视为 n-of-1 p 值，因为它的计算方式与 p 相同，同时让 n 等于 1。我们展示了 D 值如何与辨别概率和上面的区域相关。受试者工作特征（ROC）曲线。 D 值有一个明确的解释，即治疗后病情恶化的患者比例，因此有助于权衡不同情况下发生事件的可能性。 [2015 年 1 月收到。2015 年 6 月修订。]