> For the complete documentation index, see [llms.txt](https://help.connected.illumina.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/statistics/differential-analysis/kruskal-wallis-1.md).
# Kruskal-Wallis / Wilcoxon
Both Kruskal-Wallis and Wilcoxon tests are rank tests, such rank-based tests are generally advised for use with larger sample sizes. They both can only take one factor into account at a time. Kruskal-Wallis can perform on an attribute with two or more subgroups.
Wilcoxon test is a close alternative to Kruskal-wallis task, match the results of [scany Wilcoxon method](https://scanpy.readthedocs.io/en/stable/generated/scanpy.tl.rank_genes_groups.html). This test is also called "Wilcoxon Rank-Sum Test" or "Mann-Whitney U Test". When you perform comparisons on the two groups, it will filter only include the two groups first and then perform the differential analysis.
## Running the task
To invoke the Kruskal-Wallis test, select any count-based data nodes, these include:
* Gene counts
* Transcript counts
* Normalized counts
After clicking on the chosen node:
* Select *Statistics > Differential analysis* in the context-sensitive menu.
* Select *Kruskal-Wallis or Wilcoxon.*
* Select a specific factor for analysis and click the **Next** button to setup the comparisons.
{% hint style="info" %}
**Note**: Wilcoxon test will filter the data to include the observations in the two comparison groups to generate p-value, while Kruskal-Wallis will use all the samples in the input data to generate p-value on the selected attribute.
{% endhint %}
* Define the comparisons by dragging and dropping each group in the *Denominator* and *Numerator* boxes and click **Add comparison**
If the data has not been filtered upstream the **Low value filter** box will be checked by default. Similarly, the **Default** normalisation will be selected if the software detects that the data as not been previously normalised.
## Advanced option
If there are tied ranks of feature expression values, the default is not use tie correction which is corresponding to the scanpy.tl.rank\_genes\_groups(tie\_correct = False).
The results of the analysis will appear similar to other [ANOVA/LIMMA-trend/LIMMA-voom](/icm/analyses/analysis-functionality/task-menu/statistics/differential-analysis/anova-limma-trend-limma-voom.md). However, the column to indicate mean expression levels for each group will display the median instead for Kruskal-Wallis.
---
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