A dirty dozen:: Twelve P-value misconceptions
A dirty dozen:: Twelve P-value misconceptions
复制标题
DOI：
10.1053/j.seminhematol.2008.04.003
复制
发表时间：
2008-07-01
影响因子：
3.6
通讯作者：
Goodman, Steven
中科院分区：
文献类型：
--
作者：
Goodman, Steven
P值是一种统计证据的度量，几乎出现在所有医学研究论文中。它的解释是非常困难的，因为它不是任何正式的统计推断系统的一部分。因此，P值的推论意义被广泛地、经常地误解，这一事实至少从20世纪40年代起就在无数的论文和书籍中被指出。这篇评论回顾了十几个常见的误解，并解释了为什么每一个都是错误的。它还审查了这些对其含义的不适当理解或表述可能产生的后果。最后，它将P值与其贝叶斯对应物贝叶斯因子进行了对比，贝叶斯因子几乎具有P值所缺乏的证据度量的所有理想属性，最显着的是可解释性。这一系列P值错误概念的最严重后果是错误地认为，结论错误的概率可以从单个实验的数据中计算出来，而无需参考外部证据或潜在机制的可验证性。
The P value is a measure of statistical evidence that appears in virtually all medical research papers. Its interpretation is made extraordinarily difficult because it is not part of any formal system of statistical inference. As a result, the P value's inferential meaning is widely and often wildly misconstrued, a fact that has been pointed out in innumerable papers and books appearing since at least the 1940s. This commentary reviews a dozen of these common misinterpretations and explains why each is wrong. It also reviews the possible consequences of these improper understandings or representations of its meaning. Finally, it contrasts the P value with its Bayesian counterpart, the Bayes' factor, which has virtually all of the desirable properties of an evidential measure that the P value lacks, most notably interpretability. The most serious consequence of this array of P-value misconceptions is the false belief that the probability of a conclusion being in error can be calculated from the data in a single experiment without reference to external evidence or the plausibility of the underlying mechanism.