Power Analysis for Prediction-Powered Inference
Modern studies increasingly leverage outcomes predicted by machine learning and artificial intelligence (AI/ML) models, and recent work, such as prediction-powered inference (PPI), has developed valid downstream statistical inference procedures. However, classical power and sample size formulas do not readily account for these predictions. In this work, we tackle a simple yet practical question: given a new AI/ML model with high predictive power, how many labeled samples are needed to achieve a desired level of statistical power? We derive closed-form power formulas by characterizing the asymptotic variance of the PPI estimator and applying Wald test inversion to obtain the required label...
| Authors | Yiqun T. Chen, Moran Guo, Shengy Li |
|---|---|
| Primary category | stat.ME |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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