P-values - a chronic conundrum

In medical research and practice, the p-value is arguably the most often used statistic and yet it is widely misconstrued as the probability of the type I error, which comes with serious consequences. This misunderstanding can greatly affect the reproducibility in research, treatment selection in medical practice, and model specification in empirical analyses. By using plain language and concrete examples, this paper is intended to elucidate the p-value confusion from its root, to explicate the difference between significance and hypothesis testing, to illuminate the consequences of the confusion, and to present a viable alternative to the conventional p-value.

The confusion with p-values has plagued the research community and medical practitioners for decades. However, efforts to clarify it have been largely futile, in part, because intuitive yet mathematically rigorous educational materials are scarce. Additionally, the lack of a practical alternative to the p-value for guarding against randomness also plays a role. The p-value confusion is rooted in the misconception of significance and hypothesis testing. Most, including many statisticians, are unaware that p-values and significance testing formed by Fisher are incomparable to the hypothesis testing paradigm created by Neyman and Pearson. And most otherwise great statistics textbooks tend to cobble the two paradigms together and make no effort to elucidate the subtle but fundamental differences between them. The p-value is a practical tool gauging the "strength of evidence" against the null hypothesis. It informs investigators that a p-value of 0.001, for example, is stronger than 0.05. However, p-values produced in significance testing are not the probabilities of type I errors as commonly misconceived. For a p-value of 0.05, the chance a treatment does not work is not 5%; rather, it is at least 28.9%.

A long-overdue effort to understand p-values correctly is much needed. However, in medical research and practice, just banning significance testing and accepting uncertainty are not enough. Researchers, clinicians, and patients alike need to know the probability a treatment will or will not work. Thus, the calibrated p-values (the probability that a treatment does not work) should be reported in research papers.