Wilcoxon signed-rank test in SciPy - Mini Project: Build & Apply

Wilcoxon Signed-Rank Test with SciPy

📖 Scenario: You are a data analyst working with a health study team. They collected blood pressure readings from 6 patients before and after a new treatment. You need to check if the treatment caused a significant change in blood pressure.

🎯 Goal: Build a Python program that uses the Wilcoxon signed-rank test to compare paired blood pressure readings before and after treatment.

📋 What You'll Learn

Create two lists called before and after with exact blood pressure values.

Create a variable called alpha and set it to 0.05 for significance level.

Use scipy.stats.wilcoxon to perform the Wilcoxon signed-rank test on before and after.

Print the test statistic and p-value with clear labels.

💡 Why This Matters

🌍 Real World

The Wilcoxon signed-rank test is used in medical studies, psychology, and other fields to compare paired samples when data may not be normally distributed.

💼 Career

Data analysts and scientists use this test to validate if treatments or interventions cause significant changes in paired observations.

1

Create blood pressure data lists

Create two lists called before and after with these exact values: before = [120, 130, 115, 140, 125, 135] and after = [118, 128, 117, 135, 130, 133].

SciPy

# Create the lists before and after with the exact values
# Your code here

Need a hint?

Use square brackets to create lists and separate values with commas.

2

Set significance level

Create a variable called alpha and set it to 0.05 to represent the significance level.

SciPy

before = [120, 130, 115, 140, 125, 135]
after = [118, 128, 117, 135, 130, 133]
# Create alpha variable and set it to 0.05
# Your code here

Need a hint?

Just assign 0.05 to a variable named alpha.

3

Perform Wilcoxon signed-rank test

Import wilcoxon from scipy.stats and use it to perform the Wilcoxon signed-rank test on the before and after lists. Store the result in a variable called test_result.

SciPy

before = [120, 130, 115, 140, 125, 135]
after = [118, 128, 117, 135, 130, 133]
alpha = 0.05
# Import wilcoxon and perform the test
# Your code here

Need a hint?

Use from scipy.stats import wilcoxon and call wilcoxon(before, after).

4

Print test statistic and p-value

Print the test statistic and p-value from test_result with labels exactly as: Test statistic: and p-value:.

SciPy

from scipy.stats import wilcoxon

before = [120, 130, 115, 140, 125, 135]
after = [118, 128, 117, 135, 130, 133]
alpha = 0.05

test_result = wilcoxon(before, after)
# Print the test statistic and p-value with labels
# Your code here

Need a hint?

Use print(f"Test statistic: {test_result.statistic}") and similarly for p-value.