Wilcoxon SignedRank Test in Python
Have you ever wondered how to conduct a Wilcoxon SignedRank Test in Python? The Wilcoxon SignedRank Test is a nonparametric test used to compare two related samples.
It is especially useful when the assumption of normality is not met, and it is a good alternative to the paired samples ttest.
Steps to conduct a Wilcoxon SignedRank Test in Python
The first step is to import necessary libraries like numpy and scipy(stats).
After importing the required libraries, you need to create data to conduct the test. You can use the wilcoxon function to perform this test.
As with most statistical tests, it is essential to follow certain steps to perform a Wilcoxon SignedRank Test.

Define your null and alternate hypotheses
The null hypothesis is that there is no difference between the two related samples.
The alternate hypothesis is that there is a significant difference between the two related samples.

Import necessary libraries and load data
Before you can perform the test, you need to import the necessary libraries, load the data, and clean it up if required. Once you have the data, you are ready to analyze it.

Determine the test statistic and critical value
To perform the test, you need to calculate a test statistic and a critical value.
The test statistic is calculated using the difference between the two related samples. If the absolute value of the test statistic is greater than the absolute value of the critical value, then the null hypothesis can be rejected.

Calculate the pvalue
The pvalue is the probability of obtaining a test statistic as extreme as the one calculated, assuming the null hypothesis is true.
If the pvalue is less than the significance level, usually 0.05, then the null hypothesis can be rejected.

Interpret the results
Once you have calculated the test statistic, critical value, and pvalue, you can use these values to interpret the results and draw conclusions about the data.
Example: Wilcoxon SignedRank Test in Python
Background and Research Question
Suppose you own a car dealership and want to determine whether a particular fuel treatment increases the average miles per gallon (mpg) of cars in your lot. You have collected data on two groups: Group 1 consists of cars with no fuel treatment, and Group 2 consists of cars that have had the fuel treatment.
Data Creation
To conduct the test, you first need to create the data to analyze. For this example, let’s say that Group 1 consists of 10 cars, and Group 2 consists of 12 cars.
The mpg values for each group are listed below:
Group 1: 30, 28, 35, 22, 25, 24, 18, 29, 27, 21
Group 2: 32, 33, 34, 35, 36, 28, 26, 30, 31, 29, 28, 30
Conducting a Wilcoxon SignedRank Test and Its Syntax
To conduct the test, import the Stats module of scipy library along with numpy. Load the data into pandas data frame, and use scipy.stats.wilcoxon function to calculate the Wilcoxon signedrank test.
Syntax:
from scipy import stats
stats.wilcoxon(x, y=None, zero_method='wilcox', correction=False, alternative='twosided')
where,
 x,y array_like : 1Dimensional Sequence of observations. y is optional.
 zero_method : {‘pratt’, ‘wilcox’, ‘zsplit’}, optional. Default is “wilcox”.
 correction : bool, optional. Default is False.
 alternative : {‘twosided’, ‘less’, ‘greater’}, optional. Default is “twosided”.
Please note that the syntax being used differs from case to case. Interpretation of Results and Null/Alternative Hypotheses
After running the Wilcoxon SignedRank Test in Python using the data from Group 1 and Group 2, the pvalue comes out to be 0.044.
It is less than the significance level of 0.05, which means we can reject the null hypothesis. Therefore, we can conclude that the fuel treatment improves the average mpg of cars.
To Conclude
In this article, we discussed the Wilcoxon SignedRank Test in Python, which is a nonparametric test that compares two related samples. We discussed the steps to conduct the test and demonstrated the test’s significance with respect to fuel treatment and mpg values.
Remember to always follow the necessary steps when performing this test, and make sure to interpret the results correctly to draw accurate conclusions. In conclusion, this article discussed the Wilcoxon SignedRank Test in Python, which is a nonparametric test used to compare two related samples.
We outlined the steps to conduct the test and provided an example of how to apply this test in a realworld scenario, specifically regarding fuel treatment and mpg values. It is crucial to follow the necessary steps while performing this test, interpret the results accurately, and draw conclusions based on the data.
The Wilcoxon SignedRank Test is a valuable tool for anyone looking to analyze paired data when the assumption of normality is not met.