Wrangling with p-values versus effect sizes to improve medical decision-making: A tutorial

The most pervasive and damaging myth in clinical research is that the smaller the p-value, the stronger the hypothesis. In reality, the p-value primarily reflects the quality of research design decisions. The most common proposal to avoid misleading conclusions from clinical research requires the appropriate use of effect sizes, but which effect size, used when and how, is an open question. A solution is proposed for perhaps the most common problem in clinical research, the comparison between two populations, for example, comparison of two treatments in a randomized clinical trial or comparison of high risk versus low risk individuals in an epidemiological study: the success rate difference or equivalently the number needed to treat/take (NNT).