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How to Perform Dunn’s Test in Python

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  • Last Updated : 19 Apr, 2022
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Dunn’s test should be used to establish which groups are distinct If the Kruskal-Wallis test yields statistically significant findings. After your  ANOVA has revealed a noticeable difference in three or more means, you may apply Dunn’s Test to determine which particular means are different from the rest. Dunn’s Multiple Comparison Test is a non-parametric post hoc, non-parametric test that doesn’t presume your data comes from a certain distribution.

To perform the duns test user neesneedsds to call the  posthoc_dunn() function from the scikit-posthocs library. 

posthoc_dunn() Function:

Syntax:

scikit_posthocs.posthoc_dunn(a, val_col: str = None, group_col: str = None, p_adjust: str = None, sort: bool = True)

Parameters:

  • a : it’s an array type object or a dataframe object or series.
  •  group_col : column of the predictor or the dependent variable
  • p_adjust: P values can be adjusted using this method. it’s a string type possible values are :
    • ‘bonferroni’
    • hommel
    • holm-sidak
    • holm
    • simes-hochberg and more…

Returns: p-values.

Syntax to install posthocs library:

pip install scikit-posthocs

This is a hypotheses test and the two hypotheses are as follows:

  • Null hypothesis:  The given sample have the same median
  • Alternative hypothesis:  The given sample has a different median.

In this example, we import the packages, read the iris CSV  file, and use posthoc_dunn() function to perform dunns test. dunn’s test is performed on the sepal width of the three plant species. 

Click here to view and download the CSV file.

Python3




# importing packages and modules
import pandas as pd
import scikit_posthocs as sp
 
# reading CSV file
dataset= pd.read_csv('iris.csv')
 
# data which contains sepal width of the three species
data = [dataset[dataset['species']=="setosa"]['sepal_width'],
        dataset[dataset['species']=="versicolor"]['sepal_width'],
        dataset[dataset['species']=="virginica"]['sepal_width']]
 
# using the posthoc_dunn() function
p_values= sp.posthoc_dunn(data, p_adjust = 'holm')
 
print(p_values)


Output:

  • For the difference between groups 1 and 2, the adjusted p-value is 3.247311e-14
  • For the difference between groups 2 and 3, the adjusted p-value is 1.521219e-02

We further check if p_values are higher than the level of significance. false represents that two groups are statistically significant or that the null hypothesis is rejected.

Python3




p_values > 0.05


 
 

Output:

 

 

We take the level of significance to be 0.05 in this example. no two groups (species)  are statistically significant as no two groups have a p_value more than 0.05. hence, we can say the null hypothesis is false, and the alternative hypothesis is true. 

 


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