The extent and consequences of p-hacking in science.
| Autor: | Megan L Head, Luke Holman, Rob Lanfear, Andrew T Kahn, Michael D Jennions |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | PLoS Biology, Vol 13, Iss 3, p e1002106 (2015) |
| Druh dokumentu: | article |
| ISSN: | 1544-9173 1545-7885 |
| DOI: | 10.1371/journal.pbio.1002106 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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