# Monte Carlo error analyses of Spearman's rank test

arXiv: https://arxiv.org/abs/1411.3816 · HTML: https://mlsanity.com/paper/1411.3816/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2014.

## Built on by (most cited first, 2 of 2)

- [The Effect of Data Dimensionality on Neural Network Prunability](https://mlsanity.com/paper/2212.00291/) (2022)
- [Revisiting mass-radius relationships for exoplanet populations: a machine learning insight](https://mlsanity.com/paper/2301.07143/) (2023)

## Abstract (arXiv)

Spearman's rank correlation test is commonly used in astronomy to discern whether a set of two variables are correlated or not. Unlike most other quantities quoted in astronomical literature, the Spearman's rank correlation coefficient is generally quoted with no attempt to estimate the errors on its value. This is a practice that would not be accepted for those other quantities, as it is often regarded that an estimate of a quantity without an estimate of its associated uncertainties is meaningless. This manuscript describes a number of easily implemented, Monte Carlo based methods to estimate the uncertainty on the Spearman's rank correlation coefficient, or more precisely to estimate its probability distribution.