Apprendre Challenge: Analyze A/B Test Results | Product Experimentation and Hypothesis Testing

As you work with product experiments, it is crucial to accurately interpret the results of A/B tests to inform your product decisions. To recap, the conversion rate for each group is calculated as the number of conversions divided by the total number of users in that group. Once you have these rates, you can use a statistical test to determine if the difference between the groups is meaningful. The independent t-test from the scipy.stats module is commonly used for this purpose, as it helps you assess whether the observed difference in conversion rates is statistically significant and unlikely to be due to random chance. A low p-value (commonly below 0.05) suggests you can be confident in the result.


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from scipy.stats import ttest_ind

# Example conversion data for control and variant groups
control_conversions = [0, 1, 0, 1, 0, 0, 1, 0, 1, 0]
variant_conversions = [1, 1, 1, 0, 1, 1, 0, 1, 1, 1]

control_rate = sum(control_conversions) / len(control_conversions)
variant_rate = sum(variant_conversions) / len(variant_conversions)

t_stat, p_value = ttest_ind(control_conversions, variant_conversions)

summary = (
    f"Control conversion rate: {control_rate:.2%}\n"
    f"Variant conversion rate: {variant_rate:.2%}\n"
    f"T-test p-value: {p_value:.4f}\n"
)

if p_value < 0.05:
    summary += "Result: Statistically significant difference detected. Recommend adopting the variant."
else:
    summary += "Result: No statistically significant difference detected. Recommend keeping the control."

print(summary)

Tâche

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Write a function that takes two lists representing conversions for a control group and a variant group in an A/B test and prints a summary for a product decision meeting.

Calculate the conversion rate for the control group.
Calculate the conversion rate for the variant group.
Use ttest_ind from scipy.stats to determine the p-value for the difference in conversion rates.
Print a summary that includes both conversion rates, the p-value, and a product recommendation based on the statistical significance.

Solution

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Section 2. Chapitre 3

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from scipy.stats import ttest_ind

# Example conversion data for control and variant groups
control_conversions = [0, 1, 0, 1, 0, 0, 1, 0, 1, 0]
variant_conversions = [1, 1, 1, 0, 1, 1, 0, 1, 1, 1]

control_rate = sum(control_conversions) / len(control_conversions)
variant_rate = sum(variant_conversions) / len(variant_conversions)

t_stat, p_value = ttest_ind(control_conversions, variant_conversions)

summary = (
    f"Control conversion rate: {control_rate:.2%}\n"
    f"Variant conversion rate: {variant_rate:.2%}\n"
    f"T-test p-value: {p_value:.4f}\n"
)

if p_value < 0.05:
    summary += "Result: Statistically significant difference detected. Recommend adopting the variant."
else:
    summary += "Result: No statistically significant difference detected. Recommend keeping the control."

print(summary)