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PLoS Biol. 2015 Mar 13;13(3):e1002106. doi: 10.1371/journal.pbio.1002106. eCollection 2015 Mar.

The extent and consequences of p-hacking in science.

Author information

1
Division of Evolution, Ecology and Genetics, Research School of Biology, Australian National University, Acton, Canberra, Australia.
2
Division of Evolution, Ecology and Genetics, Research School of Biology, Australian National University, Acton, Canberra, Australia; Department of Biological Sciences, Faculty of Science, Macquarie University, North Ryde, New South Wales, Australia.

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.

PMID:
25768323
PMCID:
PMC4359000
DOI:
10.1371/journal.pbio.1002106
[Indexed for MEDLINE]
Free PMC Article

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