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P Value: Significance Is Not All Black and White.

The P value has become an ingrained metric to determine the validity of scientific inquiry. Statistically significant results (as defined by a P value < 0.05) have been shown to be 3 times more likely to be published compared with articles with null results. Acceptance of a study for publication is often determined by the presence of a “significant” P value; however, many statisticians have raised questions of the overreliance on P values in current studies. In part because of this issue, ClinicalTrials.gov, a registry of clinical trials, was instituted to increase transparency and avoid this type of publication bias. The P value was introduced in 1925 by British statistician, Ronald Fisher, as an index measuring the strength of evidence against the null hypothesis and to determine statistical significance. The null hypothesis proposes that no statistical significance exists in a set of given observations. Fisher advocated P less than 0.05 (5% significance) as a standard level for concluding that there is evidence against the null hypothesis being tested. It should be strongly noted that the P value was intended to accept or reject the null hypothesis and was intended only to be used in inquiry dependent on a null hypothesis. For many years, there has been much debate within the statistical community regarding the dependency on and credence given to the P value and often misunderstanding of its meaning. This is not surprising because the P value was never intended to be used as the final or only criteria for scientific inquiry. Recently, the debate has become so heated that several journals have considered banning sole reporting of the P values in the articles they accept. In an unprecedented action, the American Statistical Association, theworld's largest community of statisticians, including members from 90 countries convened a panel to render a policy statement on the “P value and statistical significance” with 6 principles underlying the proper use and interpretation of the P value. The 6 principles include: