Doing science is expensive, so a study that reveals significant results yet cannot be replicated by other investigators, represents a lost opportunity to invest those resources elsewhere. At the same time, the pressure on researchers to publish is immense.

These are the tensions that underlie the current debate about how to resolve issues surrounding the use of the p-value and the infamous significance threshold of 0.05. This measurement was adopted in the early 20^{th} century to indicate the probability that the observed results are obtained by chance variation, and the 0.05 threshold has been with it since the beginning, allowing researchers to declare as significant any effect they find that can cross that threshold.

This threshold was selected for convenience in a time when computation of the p-value was difficult to calculate. Our modern scientific tools have made calculation so easy, however, that it is hard to defend a 0.05 threshold as anything but arbitrary. A group of statisticians and researchers is trying to rehabilitate the p-value, at least for the time being, so that we can improve the reliability of results with minimal disruption to the scientific production system. They hope to do this by changing the threshold for statistical significance to 0.005.

In a new editorial in JAMA, Stanford researcher John Ioannidis, a famous critic of bias and irreproducibility in research, has come out in favor of this approach. His argument is pragmatic. In it, he acknowledges that misunderstandings of the p-value are common: many people believe that a result is worth acting on if it is supported by a significant p-value, without regard for the size of the effect or the uncertainty surrounding it.

Rather than reeducating everyone who ever needs to interpret scientific research, then, it is preferable to change our treatment of the threshold signaling statistical significance. Ioannidis also points to the success of genome-wide association studies, which improved in reproducibility after moving to a statistical significance threshold of p < 5 x 10^{-5}.

As Ioannidis admits, this is an imperfect solution. The proposal has set off substantial debate within the American Statistical Association. Bayesians, for example, see it as perpetuating the same flawed practices that got us into the reproducibility crisis in the first place. In an unpublished but widely circulated article from 2017 entitled Abandon Statistical Significance [pdf warning], Blakely McShane, Andrew Gelman, and others point to several problems with lowering the significance threshold that make it unsuitable for medical research.

First, they point out that the whole idea of the null hypothesis is poorly suited to medical research. Virtually anything ingested by or done to the body has downstream effects on other processes, almost certainly including the ones that any given trial hopes to measure. Therefore, using the null hypothesis as a straw man takes away the focus on what a meaningful effect size might be and how certain we are about the effect size we calculate for a given treatment.

They also argue that the reporting of a single p-value hides important decisions made in the analytic process itself, including all the different ways that the data could have been analyzed. They propose reporting all analyses attempted, in an attempt to capture the “researcher degrees of freedom” – the choices made by the analyst that affect how the results are calculated and interpreted.

Beyond these methodological issues, lowering the significance threshold could increase the costs of clinical trials. If our allowance for Type I error is reduced by an order of magnitude, our required sample size roughly doubles, holding all other parameters equal. In a regulatory environment where it costs over a billion dollars to bring a drug to market, this need for increased recruitment could drive up costs (which would need to be passed on to the consumer) and delay the health benefits of market release for good drugs. It is unclear whether these potential cost increases will be offset by the savings of researchers producing more reliable, reproducible studies earlier in the development process.

It also remains to be seen whether the lower p-value’s increased sample size requirement might dissuade pharmaceutical companies from bringing products to market that have a low marginal benefit. After all, you need a larger sample size to detect smaller effects, and that would only be amplified under the new significance thresholds. Overall, the newly proposed significance threshold interacts with value considerations in ways that are hard to predict but potentially worth watching.