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解决p值普遍存在问题的实用方法。

A practical solution to the pervasive problems of p values.

作者信息

Wagenmakers Eric-Jan

机构信息

Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.

出版信息

Psychon Bull Rev. 2007 Oct;14(5):779-804. doi: 10.3758/bf03194105.

Abstract

In the field of psychology, the practice of p value null-hypothesis testing is as widespread as ever. Despite this popularity, or perhaps because of it, most psychologists are not aware of the statistical peculiarities of the p value procedure. In particular, p values are based on data that were never observed, and these hypothetical data are themselves influenced by subjective intentions. Moreover, p values do not quantify statistical evidence. This article reviews these p value problems and illustrates each problem with concrete examples. The three problems are familiar to statisticians but may be new to psychologists. A practical solution to these p value problems is to adopt a model selection perspective and use the Bayesian information criterion (BIC) for statistical inference (Raftery, 1995). The BIC provides an approximation to a Bayesian hypothesis test, does not require the specification of priors, and can be easily calculated from SPSS output.

摘要

在心理学领域,p值零假设检验的做法一如既往地普遍。尽管如此受欢迎,或者也许正是因为如此,大多数心理学家并未意识到p值程序的统计特性。具体而言,p值基于从未观察到的数据,而这些假设数据本身又受到主观意图的影响。此外,p值并不能量化统计证据。本文回顾了这些p值问题,并用具体例子说明了每个问题。这三个问题统计学家很熟悉,但心理学家可能是首次听闻。解决这些p值问题的一个实际办法是采用模型选择的视角,并使用贝叶斯信息准则(BIC)进行统计推断(拉夫蒂,1995)。BIC提供了一种近似贝叶斯假设检验的方法,不需要指定先验概率,并且可以很容易地从SPSS输出中计算出来。

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