Python Kuipiec test

Kuiper's Test, also known as the Kuiper's Two-Sample Test or the Vargha-Delaney A12 Statistic, is a non-parametric method used to compare two groups or distributions in terms of their location or central tendency. It's particularly useful when you want to assess if there is a significant difference between the distributions without assuming any specific distributional form.

In Python, you can perform a Kuiper's Test using the `scipy.stats` library, which provides a function called `ks_2samp` for calculating the two-sample Kolmogorov-Smirnov statistic, which is closely related to Kuiper's Test. Here's how you might use it:

from scipy import stats

# Assuming you have two samples, sample1 and sample2
sample1 = [values for values in your_first_group]
sample2 = [values for values in your_second_group]

# Perform the Kuiper's Test
k_statistic, p_value = stats.ks_2samp(sample1, sample2)

# Interpret the results:
# If p_value < alpha (usually 0.05), reject the null hypothesis that the distributions are the same.

To calculate the Vargha-Delaney effect size (A12) alongside the p-value, you can use the `vargha_delaney` function from the `statsmodels.stats.nonparametric` module, although this may require an additional installation:

from statsmodels.stats.nonparametric import kruskal_kuiper, vargha_delaney

# Calculate Vargha-Delaney A12
a12, a12_critical_value = vargaha_delaney(sample1, sample2)