Leer Challenge: Validate a Product Hypothesis | Product Experimentation and Hypothesis Testing

Recapping hypothesis validation steps is essential for any product manager aiming to drive iterative product improvements. You begin by clearly stating your hypothesis, such as "Launching Feature X will increase daily user engagement." Next, you collect relevant data before and after the feature launch. Calculating the average engagement in both periods allows you to quantify any observed changes. To ensure these changes are not due to random chance, you use statistical tests—such as those provided by the scipy library—to determine significance. This process enables you to make data-driven decisions, justify further product investments, and communicate results confidently to your team. By validating hypotheses systematically, you create a feedback loop that fuels continuous product iteration and maximizes user impact.


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import numpy as np
from scipy import stats

# Hardcoded engagement data (e.g., daily active minutes per user)
before_launch = [12, 15, 14, 13, 16, 15, 14, 13, 12, 14]
after_launch = [15, 17, 16, 18, 17, 16, 18, 17, 16, 18]

# Calculate averages
avg_before = np.mean(before_launch)
avg_after = np.mean(after_launch)

# Statistical significance test
t_stat, p_value = stats.ttest_ind(after_launch, before_launch)

# Print summary for product iteration meeting
print(f"Average engagement before launch: {avg_before:.2f}")
print(f"Average engagement after launch: {avg_after:.2f}")
print(f"T-test p-value: {p_value:.4f}")

if p_value < 0.05:
    print("Result: The increase in engagement after the feature launch is statistically significant.")
else:
    print("Result: No statistically significant difference in engagement after the feature launch.")

Taak

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Write a script that validates a product hypothesis using engagement data. Use the provided lists to represent user engagement before and after a feature launch.

Calculate the average engagement using the before and after lists.
Perform a t-test using scipy.stats.ttest_ind to compare the two periods.
Print the average engagement for both the before and after periods.
Print the p-value from the t-test.
Print a result message indicating whether the difference is statistically significant, using a 0.05 threshold.

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Suggested prompts:

Can you explain what the t-test p-value means in this context?

What other statistical tests could be used for hypothesis validation?

How can I interpret the results to make product decisions?

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              1234567891011121314151617181920212223
            
import numpy as np
from scipy import stats

# Hardcoded engagement data (e.g., daily active minutes per user)
before_launch = [12, 15, 14, 13, 16, 15, 14, 13, 12, 14]
after_launch = [15, 17, 16, 18, 17, 16, 18, 17, 16, 18]

# Calculate averages
avg_before = np.mean(before_launch)
avg_after = np.mean(after_launch)

# Statistical significance test
t_stat, p_value = stats.ttest_ind(after_launch, before_launch)

# Print summary for product iteration meeting
print(f"Average engagement before launch: {avg_before:.2f}")
print(f"Average engagement after launch: {avg_after:.2f}")
print(f"T-test p-value: {p_value:.4f}")

if p_value < 0.05:
    print("Result: The increase in engagement after the feature launch is statistically significant.")
else:
    print("Result: No statistically significant difference in engagement after the feature launch.")