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.

Usage Notes
Data from: The extent and consequences of p-hacking in scienceThis zip file consists of three parts. 1. Data obtained from text-mining and associated analysis files. 2. Data obtained from previously published meta-analyses and associated analysis files. 3. Analysis files used to conduct meta-analyses of the data. Read me files are contained within this zip file.FILES_FOR_DRYAD.zip

聚焦于新颖、确证性和具有统计学意义的成果，导致科学文献中存在显著的偏差。一种被称为“p-hacking”的偏差类型，发生在研究人员收集或选择数据或进行统计分析，直至非显著性结果变为显著性。在此，我们通过文本挖掘展示p-hacking在科学领域中的普遍性。随后，我们阐述如何在执行荟萃分析时检测p-hacking，并表明虽然p-hacking可能普遍存在，但其影响相对于所测量的真实效应量似乎较弱。这一结果表明，p-hacking可能并未显著改变从荟萃分析中得出的科学共识。

使用说明
数据来源：科学中p-hacking的范围和后果
此zip文件包含三部分。1. 来自文本挖掘及其相关分析文件的数据。2. 来自先前发表荟萃分析及其相关分析文件的数据。3. 用于对数据进行荟萃分析的分析文件。read me文件包含在此zip文件中.FILES_FOR_DRYAD.zip