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The Extent and Consequences of P-Hacking in Science

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Listed:
  • Megan L Head
  • Luke Holman
  • Rob Lanfear
  • Andrew T Kahn
  • Michael D Jennions
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.

Suggested Citation

  • Megan L Head & Luke Holman & Rob Lanfear & Andrew T Kahn & Michael D Jennions, 2015. "The Extent and Consequences of P-Hacking in Science," PLOS Biology, Public Library of Science, vol. 13(3), pages 1-15, March.
    • Handle: RePEc:plo:pbio00:1002106
    DOI: 10.1371/journal.pbio.1002106
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    References listed on IDEAS

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