The Extent and Consequences of P-Hacking in Science
Web of Science
AuthorHead, ML; Holman, L; Lanfear, R; Kahn, AT; Jennions, MD
Source TitlePLoS Biology
PublisherPUBLIC LIBRARY SCIENCE
University of Melbourne Author/sHolman, Luke
AffiliationSchool of BioSciences
Document TypeJournal Article
CitationsHead, M. L., Holman, L., Lanfear, R., Kahn, A. T. & Jennions, M. D. (2015). The Extent and Consequences of P-Hacking in Science. PLOS BIOLOGY, 13 (3), https://doi.org/10.1371/journal.pbio.1002106.
Access StatusOpen Access
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.
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