Sifting the evidence—what's wrong with significance tests?

Abstract

P values, or significance levels, measure the strength of the evidence against the null hypothesis; the smaller the P value, the stronger the evidence against the null hypothesis An arbitrary division of results, into “significant” or “non-significant” according to the P value, was not the intention of the founders of statistical inference A P value of 0.05 need not provide strong evidence against the null hypothesis, but it is reasonable to say that P<0.001 does. In the results sections of papers the precise P value should be presented, without reference to arbitrary thresholds Results of medical research should not be reported as “significant” or “non-significant” but should be interpreted in the context of the type of study and other available evidence. Bias or confounding should always be considered for findings with low P values To stop the discrediting of medical research by chance findings we need more powerful studies