title: Statistical significance--a misconstrued notion in medical research.
abstract: 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.
authors.affiliation: Department of Epidemiology and Biometry, Finnish Institute of Occupational Health, Helinski, Finland.
authors.forename: M
authors.initials: M
authors.lastname: Nurminen
mesh_terms: D001499:Bayes Theorem; D015984:Causality; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D011336:Probability; D012107:Research Design / Q000592:standards