Data from: 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.