@article{headExtentConsequencesPhacking2015,
  title = {The Extent and Consequences of P-Hacking in Science},
  author = {Head, Megan L. and Holman, Luke and Lanfear, Rob and Kahn, Andrew T. and Jennions, Michael D.},
  date = {2015-03},
  journaltitle = {PLoS Biology},
  volume = {13},
  pages = {e1002106+},
  issn = {1545-7885},
  doi = {10.1371/journal.pbio.1002106},
  url = {https://doi.org/10.1371/journal.pbio.1002106},
  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.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13550736,~to-add-doi-URL,p-value,publication-bias,science-ethics,statistics},
  number = {3}
}

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