The Extent and Consequences of P-Hacking in Science

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. © 2015 head et al.