Work

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

Year: 2017

Type: editorial

Abstract: 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.

Source: American Journal of Epidemiology

DOI: 10.1093/aje/kwx258

Author: Clarice R. Weinberg

Institution: National Institute of Environmental Health Sciences

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,856

Funder: National Institutes of Health