Why are replication rates so low?
为何研究复现率如此低？AI 解读 ↓
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为何研究复现率如此低？
- 研究问题
- 即使原始研究的效应估计无偏，按其点估计设定复现样本量，是否仍会使实际复现率低于名义功效目标？
- 方法与思路
- 作者在简单发表过程模型中分析效应估计误差与功效函数非线性的作用，并用简约模型解释观测到的复现率。
- 材料陈述的贡献
- 摘要称忽略原始效应的估计不确定性会系统性降低复现率；其模型能够解释若干学科中的全部或部分复现缺口。
- 阅读关注点
- 应核对复现成功的定义、发表过程模型与样本量规则，以及经验拟合能否区分该机制和其他复现失败解释。
- 阅读边界
- 仅依据所给 OpenAlex 摘要；模型参数、数据和实务建议未说明，拟合复现率本身不能证明其他机制没有作用。
原文摘要
Many explanations have been offered for why replication rates are low in the social sciences, including selective publication, p -hacking, and treatment effect heterogeneity. This article emphasizes that issues with the most commonly used approach for setting sample sizes in replication studies may also play an important role. Theoretically, I show in a simple model of the publication process that we should expect the replication rate to fall below its nominal target, even when original studies are unbiased. The main mechanism is that the most commonly used approach for setting the replication sample size does not properly account for the fact that original effect sizes are estimated. Specifically, it sets the replication sample size to achieve a nominal power target under the assumption that estimated effect sizes correspond to fixed true effects. However, since there are non-linearities in the replication power function linking original effect sizes to power, ignoring the fact that effect sizes are estimated leads to systematically lower replication rates than intended. Empirically, I find that a parsimonious model accounting only for these issues can fully explain observed replication rates in experimental economics and social science, and two-thirds of the replication gap in psychology. I conclude with practical recommendations for replicators .