The Extent and Consequences of P-Hacking in Science
Abstrak
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.
Penulis (5)
M. Head
L. Holman
R. Lanfear
A. Kahn
M. Jennions
Akses Cepat
- Tahun Terbit
- 2015
- Bahasa
- en
- Total Sitasi
- 1100×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1371/journal.pbio.1002106
- Akses
- Open Access ✓