.png){kind=icon}
返回
Detecting p-Hacking
.png){kind=icon}
PRE
AI
DOI：10.3982/ECTA18583.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We theoretically analyze the problem of testing for p-hacking based on distributions of p-values across multiple studies. We provide general results for when such distributions have testable restrictions (are non-increasing) under the null of no p-hacking. We find novel additional testable restrictions for p-values based on t-tests. Specifically, the shape of the power functions results in both complete monotonicity as well as bounds on the distribution of p-values. These testable restrictions result in more powerful tests for the null hypothesis of no p-hacking. When there is also publication bias, our tests are joint tests for p-hacking and publication bias. A reanalysis of two prominent data sets shows the usefulness of our new tests.
Keyword:
p-values
p-curve
complete monotonicity
publication bias
期刊
IF:
7.1
论文数:
3.0K
被引数:
4.3W
机构
引用论文
Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics.png)
AMERICAN ECONOMIC REVIEW
AMERICAN ECONOMIC REVIEWIF11.6
PREAI
分享收藏
查看更多内容