P-values and significance in Data Analysis Python - Mini Project: Build & Apply

Understanding P-values and Significance in Data Analysis

📖 Scenario: You are a data analyst working with a small dataset of exam scores from two different teaching methods. You want to find out if the difference in average scores between the two methods is significant or could have happened by chance.

🎯 Goal: Build a simple Python program that calculates the p-value from two groups of exam scores and decides if the difference is statistically significant.

📋 What You'll Learn

Create two lists of exam scores for Method A and Method B with exact values

Set a significance level variable called alpha to 0.05

Use the scipy.stats library to perform an independent t-test

Print the p-value and a message stating if the difference is significant or not

💡 Why This Matters

🌍 Real World

Scientists and analysts use p-values to decide if their findings are likely real or just random chance.

💼 Career

Understanding p-values is essential for data analysts, researchers, and anyone interpreting statistical test results.

1

Create exam score lists for two teaching methods

Create two lists called method_a_scores and method_b_scores with these exact values: method_a_scores = [88, 92, 85, 91, 87] and method_b_scores = [78, 81, 79, 77, 80].

Data Analysis Python

# Create two lists for exam scores of Method A and Method B
# Your code here

Hint

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

2

Set the significance level

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

Data Analysis Python

method_a_scores = [88, 92, 85, 91, 87]
method_b_scores = [78, 81, 79, 77, 80]
# Set the significance level alpha to 0.05
# Your code here

Hint

The significance level is usually set to 0.05 in many tests.

3

Perform an independent t-test to calculate the p-value

Import ttest_ind from scipy.stats and use it to calculate the t-test between method_a_scores and method_b_scores. Store the p-value in a variable called p_value.

Data Analysis Python

method_a_scores = [88, 92, 85, 91, 87]
method_b_scores = [78, 81, 79, 77, 80]
alpha = 0.05
# Import ttest_ind and calculate p_value
# Your code here

Hint

Use from scipy.stats import ttest_ind and then call ttest_ind(list1, list2).

4

Print the p-value and significance result

Write code to print the p-value with the text "P-value:". Then use an if statement to print "The difference is significant." if p_value is less than alpha, otherwise print "The difference is not significant.".

Data Analysis Python

from scipy.stats import ttest_ind

method_a_scores = [88, 92, 85, 91, 87]
method_b_scores = [78, 81, 79, 77, 80]
alpha = 0.05

stat, p_value = ttest_ind(method_a_scores, method_b_scores)
# Print the p-value and check if it is less than alpha
# Your code here

Hint

Use print(f"P-value: {p_value}") and an if statement to compare p_value and alpha.