Nurminen M
Department of Epidemiology and Biometry, Finnish Institute of Occupational Health, Helinski, Finland.
Scand J Work Environ Health. 1997 Jun;23(3):232-5.
The P-value is the significance probability of obtaining a value of the test statistic that is as extreme, in relation to the null hypothesis, as that observed. Medical researchers may, in some situations, disagree on its appropriate use or on its interpretation as a summary measure of consistency with the null hypothesis in a particular data set. More informative statistical measures such as the likelihood ratio and the Bayesian posterior probability have been suggested for drawing inferences from clinical trials and epidemiologic studies. Causal inference is not statistical in nature; rather it strives to provide scientific explanations or criticisms of proposed explanations that would describe the observed data pattern. In this context, it is important to remember that a finding may not be medically important, or a causal hypothesis may even not be true even if a study shows a significant P-value.
P值是在原假设的前提下，获得与所观察到的检验统计量值同样极端的值的显著概率。在某些情况下，医学研究人员可能在其恰当使用或作为特定数据集中与原假设一致性的汇总度量的解释上存在分歧。有人建议使用更具信息量的统计量，如似然比和贝叶斯后验概率，以便从临床试验和流行病学研究中进行推断。因果推断本质上并非统计学问题；相反，它致力于对所提出的解释提供科学解释或批评，这些解释将描述所观察到的数据模式。在这种背景下，重要的是要记住，即使一项研究显示出显著的P值，一个发现可能在医学上并不重要，或者一个因果假设甚至可能不成立。
