Why hypothesis testing validates claims in SciPy - See It in Action

Why hypothesis testing validates claims

📖 Scenario: Imagine you work for a company that claims their new energy drink increases alertness. You want to check if this claim is true by testing it scientifically.

🎯 Goal: You will learn how to use hypothesis testing with Python's scipy library to check if the energy drink really improves alertness compared to no drink.

📋 What You'll Learn

Create a list of alertness scores for people who drank the energy drink

Create a list of alertness scores for people who did not drink the energy drink

Set a significance level to decide when to reject the claim

Use a t-test to compare the two groups

Print the p-value and conclusion about the claim

💡 Why This Matters

🌍 Real World

Hypothesis testing helps companies and scientists check if their claims about products or treatments are true using data.

💼 Career

Data scientists use hypothesis testing to validate assumptions and make data-driven decisions in business, healthcare, and research.

1

Create alertness scores data

Create a list called drink_group with these exact alertness scores: 85, 88, 90, 87, 92. Create another list called no_drink_group with these exact scores: 80, 82, 79, 81, 83.

SciPy

# Create the alertness scores lists
# Your code here

Need a hint?

Use square brackets to create lists with the exact numbers given.

2

Set significance level

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

SciPy

drink_group = [85, 88, 90, 87, 92]
no_drink_group = [80, 82, 79, 81, 83]
# Set the significance level
# Your code here

Need a hint?

The significance level is usually 0.05 for many tests.

3

Perform t-test to compare groups

Import ttest_ind from scipy.stats. Use ttest_ind with drink_group and no_drink_group to perform an independent t-test. Save the result in a variable called test_result.

SciPy

drink_group = [85, 88, 90, 87, 92]
no_drink_group = [80, 82, 79, 81, 83]
alpha = 0.05
# Import ttest_ind and perform the t-test
# Your code here

Need a hint?

Use from scipy.stats import ttest_ind and call ttest_ind(drink_group, no_drink_group).

4

Print p-value and conclusion

Print the p-value from test_result using print(test_result.pvalue). Then write an if statement to check if the p-value is less than alpha. If yes, print "Reject the null hypothesis: the drink improves alertness." Otherwise, print "Fail to reject the null hypothesis: no clear evidence the drink improves alertness."

SciPy

from scipy.stats import ttest_ind

drink_group = [85, 88, 90, 87, 92]
no_drink_group = [80, 82, 79, 81, 83]
alpha = 0.05

test_result = ttest_ind(drink_group, no_drink_group)
# Print the p-value and conclusion
# Your code here

Need a hint?

Use print(test_result.pvalue) and an if to compare with alpha.