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The Extent and Consequences of P-Hacking in Science

2015; Public Library of Science; Volume: 13; Issue: 3 Linguagem: Inglês

10.1371/journal.pbio.1002106

1545-7885

Megan L. Head, Luke Holman, Robert Lanfear, Andrew T. Kahn, Michael D. Jennions,

Scientific Computing and Data Management

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.