Testing for underpowered literatures
检验文献集合中的统计功效不足AI 解读 ↓
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检验文献集合中的统计功效不足
- 研究问题
- 如果一组实验都扩大样本量，预期会增加多少统计显著结果，如何在存在发表偏倚和处理效应异质性时估计这一变化？
- 方法与思路
- 作者提出调整发表偏倚的反卷积估计量，估计反事实样本扩张后的预期显著结果数，允许真实处理效应分布包含点质量。
- 材料陈述的贡献
- 摘要称，估计量渐近正态且无需限制真实效应分布；经济学期刊 RCT 应用中，样本翻倍平均增加功效 7.2 个百分点。
- 阅读关注点
- 应核对反卷积如何识别目标、发表偏倚如何调整、检验统计量分布的要求，以及样本扩张反事实和平均功效增幅的定义。
- 阅读边界
- 仅据所给 OpenAlex 摘要解读；对真实效应分布不作假设不等于方法无其他假设，所报平均增幅也不能用于预测单项实验。
原文摘要
How many experimental studies would have come to different conclusions had they been run on larger samples? I show how to estimate the expected number of statistically significant results that a set of experiments would have reported had their sample sizes all been counterfactually increased. The proposed deconvolution estimator is asymptotically normal and adjusts for publication bias. Unlike related methods, this approach requires no assumptions of any kind about the distribution of true intervention treatment effects and allows for point masses. Simulations find good coverage even when the t-score is only approximately normal. An application to randomized trials (RCTs) published in economics journals finds that doubling every sample would increase the power of t-tests by 7.2 percentage points on average. This effect is smaller than for non-RCTs and comparable to systematic replications in laboratory psychology where previous studies enabled more accurate power calculations. This suggests that RCTs are on average relatively insensitive to sample size increases. Research funders who wish to raise power should generally consider sponsoring better-measured and higher quality experiments -- rather than only larger ones.