%0 Journal Article
%T The Extent and Consequences of P-Hacking in Science
%A Andrew T. Kahn
%A Luke Holman
%A Megan L. Head
%A Michael D. Jennions
%A Rob Lanfear
%J -
%D 2015
%R 10.1371/journal.pbio.1002106
%X 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
%K Meta-analysis
%K Publication ethics
%K Binomials
%K Medicine and health sciences
%K Research validity
%K Statistical data
%K Psychology
%K Test statistics
%U https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002106