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The Extent and Consequences of P-Hacking in Science
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CC BY
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A focus on novel, confirmatory, and statistically significant results leads to substantial bias in the scientific literature. One type of bias, known as “p-hacking,” occurs when researchers collect or select data or statistical analyses until nonsignificant results become significant. Here, we use text-mining to demonstrate that p-hacking is widespread throughout science. We then illustrate how one can test for p-hacking when performing a meta-analysis and show that, while p-hacking is probably common, its effect seems to be weak relative to the real effect sizes being measured. This result suggests that p-hacking probably does not drastically alter scientific consensuses drawn from meta-analyses.

Subject:
Biology
Life Science
Material Type:
Reading
Provider:
PLOS Biology
Author:
Andrew T. Kahn
Luke Holman
Megan L. Head
Michael D. Jennions
Rob Lanfear
Date Added:
08/07/2020