The Extent and Consequences of P-Hacking in Science

Megan L Head, Luke Holman, Rob Lanfear, Andrew T Kahn and Michael D Jennions

PLOS Biology, 2015, vol. 13, issue 3, 1-15

Abstract: 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.Publication bias resulting from so-called "p-hacking" is pervasive throughout the life sciences; however, its effects on general conclusions made from the literature appear to be weak.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pbio00:1002106

DOI: 10.1371/journal.pbio.1002106

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