SciCombinator

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.

Tweets*

993

Facebook likes*

58

Reddit*

5

News coverage*

25

Blogs*

39

SC clicks

10

Concepts

Statistical power, Science, Mathematics, Meta-analysis, Effect size, Statistics, Statistical significance, Scientific method

MeSH headings

-