ArticleAmerican journal of epidemiology2017
Invited Commentary: Can Issues With Reproducibility in Science Be Blamed on Hypothesis Testing?
Article in American journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 citing papers in PubMed.
- Path dependency and the rescuing of the biomedical research enterprise.Frontiers in medical technology · 2025Review
- Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods.Environmental health perspectives · 2022Article
- Resource Sharing to Improve Research Quality.Journal of the American Heart Association · 2019Review
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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.
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