A major driver of the winner’s curse in education research is the use of underpowered trials. Many education studies are designed to look for large, impressive results. However, the true benefits of an educational intervention—known as the latent effect size—are often much smaller.
When a study has a small sample size, it lacks the statistical power to reliably detect these small true effects. If an underpowered study actually reports a large, statistically significant result, it is rarely because the intervention was a massive success. Instead, it is usually the result of measurement noise and lucky randomization. Because policy filters naturally favor and select these exaggerated results, underpowered trials make the winner’s curse significantly worse.
To solve this problem, researchers and policymakers must rethink how studies are designed and interpreted. This involves accepting smaller, more realistic effect sizes and adjusting study designs to reduce noise. Researchers might need to target more specific, homogeneous groups of students or use more precise outcome measures. Furthermore, policymakers must use statistical tools to shrink inflated estimates back to reality before making widespread decisions.
Understanding the relationship between study power, sample size, and measurement error is critical for accurately evaluating evidence-based education policy and identifying which interventions truly work.