How Many Samples Do We Need? A Toolkit for Power-Aware Evaluation Design

Abstract

If datasets are the telescopes of our field, then statistical power is their resolution, i.e., their ability to reveal a true difference in model performance when one exists. Many NLP evaluations are underpowered, leading to overstated claims of improvement. This paper introduces sk-power, an open-source Python library that helps researchers and practitioners design well-powered evaluations. Built with familiar scikit-learn-style abstractions, sk-power enables users to simulate evaluation scenarios, estimate minimum detectable effects, and assess the reliability of reported gains. We also illustrate what can go wrong when power analysis isn’t carried out. Our goal is to position power analysis as a first-class, practical step in evaluation planning.

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