Invited Commentary: Can Issues With Reproducibility in Science Be Blamed on Hypothesis Testing?

In the accompanying article (Am J Epidemiol. 2017;186(6):646-647), Dr. Timothy Lash makes a forceful case that the problems with reproducibility in science stem from our "culture" of null hypothesis significance testing. He notes that when attention is selectively given to statistically significant findings, the estimated effects will be systematically biased away from the null. Here I revisit the recent history of genetic epidemiology and argue for retaining statistical testing as an important part of the tool kit. Particularly when many factors are considered in an agnostic way, in what Lash calls "innovative" research, investigators need a selection strategy to identify which findings are most likely to be genuine, and hence worthy of further study. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.