# Testing for a difference in means of a single feature after clustering

arXiv: https://arxiv.org/abs/2311.16375 · HTML: https://mlsanity.com/paper/2311.16375/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2023.

## Built on by (most cited first, 1 of 1)

- [Hierarchical Clustering With Confidence](https://mlsanity.com/paper/2512.06522/) (2025)

## Abstract (arXiv)

For many applications, it is critical to interpret and validate groups of observations obtained via clustering. A common validation approach involves testing differences in feature means between observations in two estimated clusters. In this setting, classical hypothesis tests lead to an inflated Type I error rate. To overcome this problem, we propose a new test for the difference in means in a single feature between a pair of clusters obtained using hierarchical or k-means clustering. The test based on the proposed p-value controls the selective Type I error rate in finite samples and can be efficiently computed. We further illustrate the validity and power of our proposal in simulation and demonstrate its use on single-cell RNA-sequencing data.