Key points about Dunn’s test
- Purpose: Dunn’s test is used to identify which specific groups differ from each other when there are statistically significant differences detected between groups in the omnibus test.
- Non-parametric: Like the Kruskal-Wallis and Friedman tests, Dunn’s test is non-parametric, meaning it does not rely on assumptions about the distribution of the data.
- Procedure: Dunn’s test calculates pairwise comparisons between all groups using a rank-based approach. It computes the difference in ranks between pairs of groups and adjusts the p-values for multiple comparisons using methods such as the Bonferroni correction.
- Interpretation: If the adjusted p-value for a pairwise comparison is below a predetermined significance level (e.g., 0.05), it indicates that the difference between those two groups is statistically significant.
- Interpretation: If the adjusted p-value for a pairwise comparison is below a predetermined significance level (e.g., 0.05), it indicates that the difference between those two groups is statistically significant.
Overall, Dunn’s test provides a valuable tool for identifying specific group differences in situations where traditional parametric tests are not appropriate or when dealing with ranked data. It helps researchers gain deeper insights into the relationships between multiple groups in their data.
How to Perform Dunn’s Test in Python
Dunn’s test is a statistical procedure used for multiple comparisons following a Kruskal-Wallis test. Here’s a breakdown of what it does and when it’s used:
Table of Content
- Dunn’s Test
- What is the Kruskal-Wallis test?
- Key points about Dunn’s test
- How to Perform Dunn’s Test with Python
- Step-by-Step Guide to Perform Dunn’s Test in Python
- Frequently Asked Questions on Dunn’s Test