# NLPStatTest: A Toolkit for Comparing NLP System Performance

arXiv: https://arxiv.org/abs/2011.13231 · HTML: https://mlsanity.com/paper/2011.13231/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Why only Micro-F1? Class Weighting of Measures for Relation Classification](https://mlsanity.com/paper/2205.09460/) (2022)
- [Translation Analytics for Freelancers: I. Introduction, Data Preparation, Baseline Evaluations](https://mlsanity.com/paper/2504.14619/) (2025)
- [No Argument Left Behind: Overlapping Chunks for Faster Processing of Arbitrarily Long Legal Texts](https://mlsanity.com/paper/2410.19184/) (2024)

## Abstract (arXiv)

Statistical significance testing centered on p-values is commonly used to compare NLP system performance, but p-values alone are insufficient because statistical significance differs from practical significance. The latter can be measured by estimating effect size. In this paper, we propose a three-stage procedure for comparing NLP system performance and provide a toolkit, NLPStatTest, that automates the process. Users can upload NLP system evaluation scores and the toolkit will analyze these scores, run appropriate significance tests, estimate effect size, and conduct power analysis to estimate Type II error. The toolkit provides a convenient and systematic way to compare NLP system performance that goes beyond statistical significance testing