Interpreting null-hypothesis significance tests : p, effect size, and power
This paper aims to explain the rationale of hypothesis testing in research. Many studies have shown some confusion as to the significance of hypothesis testing results. This confusion could partially come from discrepancies between the approaches of Fisher, Neyman and Pearson as well as Bayes. The article clarifies the information provided by the significance coefficient, effect size and power, and proposes various software to perform power and effect size analysis.