Performing Wilcoxon signed-rank test in Python

After interpreting the biostatistical results, it’s always good to visualize them using histograms and check if the data follows a distribution, which is optimal for different tests and vice versa, such as, are specific tests optimal for the data we have? Why is this important? Because all statistical tests have certain assumptions and these need to be addressed. For example, Student’s t-test has assumptions that are related to the normality of the data. Also, Student’s t-test results can be affected by extreme values.

Let’s check these by plotting the histograms of the variables:

import matplotlib.pyplot as plt
# Set up the figure and axis for plotting
plt.figure(figsize=(10, 6))
# Plot histogram for Female HDL
plt.hist(HDLf, bins=20, color='blue', alpha=0.7, label='Female HDL')
# Plot histogram for Male HDL
plt.hist(HDLm, bins=20, color='orange', alpha=0.7, label=&apos...