Statistical Power Planning and Inference Reliability in Small Sample Experiments: A Policy Evaluation

Abstract

This paper presents a policy evaluation of statistical power planning and its direct impact on inference reliability within small sample experiments, a methodological challenge that increasingly threatens the validity of empirical evidence in public policy, social sciences, and clinical research. In resource-constrained or highly specialized experimental environments, small sample sizes are frequently unavoidable due to high implementation costs, ethical limitations, or small target populations. However, traditional reliance on null hypothesis significance testing without adequate power planning often yields studies that are underpowered, leading to an elevated rate of false negatives and a high risk of false positives. This paper systematically evaluates the institutional and methodological policies that govern sample size planning. Through a review of current practices, simulation-based evaluations, and a comparison of statistical paradigms, we demonstrate that conventional power estimation techniques fail to account for the unique vulnerabilities of small sample designs, such as extreme Type M (magnitude) and Type S (sign) errors. We propose a comprehensive policy framework for funding agencies, academic journals, and research institutions to mandate simulation-based power audits, pre-registered design analyses, and alternative inferential frameworks to ensure that policy decisions are built upon reproducible and robust scientific evidence.