Apprendre Designing A/B Tests with Python | Optimizing Growth Experiments

A/B testing is a core tool for growth hackers, allowing you to compare two versions of a product or experience—such as a landing page, email subject line, or signup process—to see which performs better. By randomly assigning users to either variant A or variant B and measuring their responses, you can make data-driven decisions that optimize growth strategies. A/B tests are essential for validating hypotheses and minimizing risk before rolling out changes to your user base.


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import numpy as np

# Simulate user responses for A/B test
np.random.seed(42)  # For reproducibility

n_users = 1000
conversion_rate_A = 0.12  # 12% for variant A
conversion_rate_B = 0.16  # 16% for variant B

# Simulate conversions: 1 = converted, 0 = did not convert
responses_A = np.random.binomial(1, conversion_rate_A, n_users)
responses_B = np.random.binomial(1, conversion_rate_B, n_users)

# Count conversions
conversions_A = responses_A.sum()
conversions_B = responses_B.sum()

print(f"Variant A: {conversions_A} conversions out of {n_users} users")
print(f"Variant B: {conversions_B} conversions out of {n_users} users")

After simulating user responses, you interpret the results by calculating the conversion rate for each variant. The conversion rate is simply the number of users who performed the desired action (such as signing up or making a purchase) divided by the total number of users exposed to that variant. By comparing these rates, you can determine which variant is more effective. A higher conversion rate in one group suggests that the changes made in that variant positively influenced user behavior, guiding your decision on which version to implement more broadly.


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import pandas as pd

# Summarize results in a DataFrame
results = pd.DataFrame({
    "Variant": ["A", "B"],
    "Conversions": [conversions_A, conversions_B],
    "Total Users": [n_users, n_users],
    "Conversion Rate": [conversions_A / n_users, conversions_B / n_users]
})

print(results)

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Section 3. Chapitre 1

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              12345678910111213141516171819
            
import numpy as np

# Simulate user responses for A/B test
np.random.seed(42)  # For reproducibility

n_users = 1000
conversion_rate_A = 0.12  # 12% for variant A
conversion_rate_B = 0.16  # 16% for variant B

# Simulate conversions: 1 = converted, 0 = did not convert
responses_A = np.random.binomial(1, conversion_rate_A, n_users)
responses_B = np.random.binomial(1, conversion_rate_B, n_users)

# Count conversions
conversions_A = responses_A.sum()
conversions_B = responses_B.sum()

print(f"Variant A: {conversions_A} conversions out of {n_users} users")
print(f"Variant B: {conversions_B} conversions out of {n_users} users")


              1234567891011
            
import pandas as pd

# Summarize results in a DataFrame
results = pd.DataFrame({
    "Variant": ["A", "B"],
    "Conversions": [conversions_A, conversions_B],
    "Total Users": [n_users, n_users],
    "Conversion Rate": [conversions_A / n_users, conversions_B / n_users]
})

print(results)

Tout était clair ?

Merci pour vos commentaires !

Section 3. Chapitre 1

Designing A/B Tests with Python

1. What is the primary goal of an A/B test?

2. How can Python help simulate A/B test outcomes?

3. What metric is commonly used to compare A/B test variants?

Designing A/B Tests with Python

1. What is the primary goal of an A/B test?

2. How can Python help simulate A/B test outcomes?

3. What metric is commonly used to compare A/B test variants?