For a century P -value is routinely used in almost every research paper with a threshold of 0.05 to reject the null hypothesis. The aim of this short review was to discuss the validity of this arbitrary (yet sacred) threshold. The history of P -value shows that very quickly, practitioners had found a simple method that appealed to them, while statisticians saw no great need to curb this enthusiasm, which seemed consensual. However, heavy reliance on P -values is of concern because of potential misuse and misinterpretation. The main pitfalls of P < 0.05 are the dichotomized approach with a black-or-white judgement, possible false positive results, lack of information about magnitude of the effect, clinical relevance, and use out of context. These pitfalls explain why several statisticians and researcher recommend abandoning, not the P -value itself but the threshold of 0.05 and the term “statistical significance”. We are faced with a paradigm shift by demoting P -value from its threshold-screening role and using alternative tools such as Bayesian methods, effect size with confidence intervals, more stringent thresholds, pragmatic trials, and the minimal clinically important difference. This will require statisticians, researchers, publishers, and health care decision makers to radically change the way they interpret scientific data by abandoning century-old dichotomous analysis–a true revolution to come.