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---
title: Wilcoxon
emoji: 🤗
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 3.0.2
app_file: app.py
pinned: false
tags:
- evaluate
- comparison
description: >-
Wilcoxon's test is a signed-rank test for comparing paired samples.
---
# Comparison Card for Wilcoxon
## Comparison description
Wilcoxon's test is a non-parametric signed-rank test that tests whether the distribution of the differences is symmetric about zero. It can be used to compare the predictions of two models.
## How to use
The Wilcoxon comparison is used to analyze paired ordinal data.
## Inputs
Its arguments are:
`predictions1`: a list of predictions from the first model.
`predictions2`: a list of predictions from the second model.
## Output values
The Wilcoxon comparison outputs two things:
`stat`: The Wilcoxon statistic.
`p`: The p value.
## Examples
Example comparison:
```python
wilcoxon = evaluate.load("wilcoxon")
results = wilcoxon.compute(predictions1=[-7, 123.45, 43, 4.91, 5], predictions2=[1337.12, -9.74, 1, 2, 3.21])
print(results)
{'stat': 5.0, 'p': 0.625}
```
## Limitations and bias
The Wilcoxon test is a non-parametric test, so it has relatively few assumptions (basically only that the observations are independent). It should be used to analyze paired ordinal data only.
## Citations
```bibtex
@incollection{wilcoxon1992individual,
title={Individual comparisons by ranking methods},
author={Wilcoxon, Frank},
booktitle={Breakthroughs in statistics},
pages={196--202},
year={1992},
publisher={Springer}
}
```
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