有關統計學上P值的聲明 (A statement on statistical P value)

       鑒於近期都有一些文章, 討論P值的錯誤用法等, 近日美國統計學協會就發表了一份關於P值的聲明, 較明確地澄清P值的用法及意義. 現撰稿如下:

What is a p-value? P值是什么

p-value is the probability under a specified statistical model that a statistical summary of the data (for example, the sample mean difference between two compared groups) would be equal to or more extreme than its observed value.

P值指的是在一个特定的统计模型下，数据的某个汇总指标（例如两样本的均值之差）等于观测值或比观测值更为极端的概率。

觀點：

1.P-values can indicate how incompatible the data are with a specified statistical model.

    P值可以表达的是数据与一个给定模型不匹配的程度

2.P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.

    P值并不能衡量某条假设为真的概率，或是数据仅由随机因素产生的概率

3.Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.

     科学结论、商业决策或政策制定不应该仅依赖于P值是否超过一个给定的阈值

4.Proper inference requires full reporting and transparency.

           合理的推断过程需要完整的报告和透明度

5.A p-value, or statistical significance, does not measure the size of an effect or the importance of a result.

    P值或统计显著性并不衡量影响的大小或结果的重要性

6.By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.

    P值就其本身而言，并不是一个非常好的对模型或假设所含证据大小的衡量

參考資料:

1. http://amstat.tandfonline.com/doi/abs/10.1080/00031305.2016.1154108 (完整的英文聲明)

2. http://cos.name/2016/03/asa-statement-on-p-value/#more-11902 (聲明的中文翻譯)