Evidence-based medicine or statistically manipulated medicine? Are we slaves to the P-value?

First popularized almost a century ago in epidemiologic research by Ronald Fisher and Jerzy Neyman, the P-value has become perhaps the most misunderstood and even misused statistical value or descriptor. Indeed, modern clinical research has now come to be centered around and guided by an arbitrary P-value of <0.05 as a magical threshold for significance, so much so that experimental design, reporting of experimental findings, and interpretation and adoption of such findings have become largely dependent on this "significant" P-value. This has given rise to multiple biases in the overall body of biomedical literature that threatens the very validity of clinical research. Ultimately, a drive toward reporting a "significant" P-value (by various statistical manipulations) risks creating a falsely positive body of science, leading to (i) wasted resources in pursuing fruitless research and (ii) futile or even harmful policies/therapeutic recommendations. This article reviews the history of the P-value, the conceptual basis of P-value in the context of hypothesis testing and challenges in critically appraising clinical evidence vis-à-vis the P-value. This review is aimed at raising awareness of the pitfalls of this rigid observation of the threshold of statistical significance when evaluating clinical trials and to generate discussion regarding whether the scientific body needs a rethink about how we decide clinical significance.