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name: A/B Test Analysis description: Design and analyze A/B tests, calculate statistical significance, and determine sample sizes for conversion optimization and experiment validation

A/B Test Analysis

Overview

A/B testing is a statistical method to compare two variants and determine which performs better, enabling data-driven optimization decisions.

When to Use

• Comparing two versions of a product feature, webpage, or marketing campaign
• Optimizing conversion rates, click-through rates, or user engagement metrics
• Making data-driven decisions with statistical confidence about changes
• Determining sample size requirements for experiment validity
• Analyzing treatment effects and measuring lift from interventions
• Evaluating whether observed differences are statistically significant

Core Components

• Control Group: Original version (A)
• Treatment Group: New variant (B)
• Metric: Outcome being measured
• Sample Size: Observations needed for power
• Significance Level: Type I error threshold (α = 0.05)
• Power: 1 - Type II error (typically 0.80)

Analysis Steps

1. Define success metric
2. Calculate sample size
3. Run experiment
4. Check assumptions
5. Perform statistical test
6. Calculate effect size
7. Interpret results

Implementation with Python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from scipy.stats import binom_test, ttest_ind, chi2_contingency
import seaborn as sns

# Sample A/B test data
np.random.seed(42)

# Scenario: Testing new checkout flow
control_conversions = np.random.binomial(1, 0.10, 10000)
treatment_conversions = np.random.binomial(1, 0.12, 10000)

control_revenue = np.random.exponential(50, 10000)
treatment_revenue = np.random.exponential(55, 10000)

# Create dataframes
df_control = pd.DataFrame({
    'group': 'Control',
    'converted': control_conversions,
    'revenue': control_revenue,
})

df_treatment = pd.DataFrame({
    'group': 'Treatment',
    'converted': treatment_conversions,
    'revenue': treatment_revenue,
})

df = pd.concat([df_control, df_treatment], ignore_index=True)

print("A/B Test Data Summary:")
print(df.groupby('group')[['converted', 'revenue']].agg({
    'converted': ['sum', 'count', 'mean'],
    'revenue': ['sum', 'mean', 'std'],
}))

# 1. Conversion Rate Test (Chi-square)
contingency_table = pd.crosstab(df['group'], df['converted'])
print("\nContingency Table:")
print(contingency_table)

chi2, p_value, dof, expected = chi2_contingency(contingency_table)
print(f"\nChi-square Test:")
print(f"Chi2 statistic: {chi2:.4f}")
print(f"P-value: {p_value:.4f}")
print(f"Significant: {'Yes' if p_value < 0.05 else 'No'}")

# 2. Conversion Rate Calculation
control_cr = df[df['group'] == 'Control']['converted'].mean()
treatment_cr = df[df['group'] == 'Treatment']['converted'].mean()
lift = (treatment_cr - control_cr) / control_cr * 100

print(f"\nConversion Rates:")
print(f"Control: {control_cr:.4f} ({control_cr*100:.2f}%)")
print(f"Treatment: {treatment_cr:.4f} ({treatment_cr*100:.2f}%)")
print(f"Lift: {lift:.2f}%")

# 3. Revenue Per User Test (T-test)
control_revenue = df[df['group'] == 'Control']['revenue']
treatment_revenue = df[df['group'] == 'Treatment']['revenue']

t_stat, p_value_revenue = ttest_ind(control_revenue, treatment_revenue)
print(f"\nRevenue Per User T-test:")
print(f"Control Mean: ${control_revenue.mean():.2f}")
print(f"Treatment Mean: ${treatment_revenue.mean():.2f}")
print(f"T-statistic: {t_stat:.4f}")
print(f"P-value: {p_value_revenue:.4f}")
print(f"Significant: {'Yes' if p_value_revenue < 0.05 else 'No'}")

# 4. Effect Size (Cohen's d)
def cohens_d(group1, group2):
    n1, n2 = len(group1), len(group2)
    var1, var2 = np.var(group1, ddof=1), np.var(group2, ddof=1)
    pooled_std = np.sqrt(((n1-1)*var1 + (n2-1)*var2) / (n1+n2-2))
    return (np.mean(group1) - np.mean(group2)) / pooled_std

effect_size = cohens_d(control_revenue, treatment_revenue)
print(f"\nEffect Size (Cohen's d): {effect_size:.4f}")
print("Interpretation: " + {
    True: "Small effect (|d| < 0.2)",
    False: {
        True: "Medium effect (0.2 <= |d| < 0.8)",
        False: "Large effect (|d| >= 0.8)"
    }[abs(effect_size) < 0.8]
}[abs(effect_size) < 0.2])

# 5. Confidence Intervals
def confidence_interval(data, confidence=0.95):
    n = len(data)
    mean = np.mean(data)
    se = stats.sem(data)
    margin = se * stats.t.ppf((1 + confidence) / 2, n - 1)
    return mean - margin, mean + margin

ci_control = confidence_interval(control_revenue)
ci_treatment = confidence_interval(treatment_revenue)

print(f"\n95% Confidence Intervals:")
print(f"Control: (${ci_control[0]:.2f}, ${ci_control[1]:.2f})")
print(f"Treatment: (${ci_treatment[0]:.2f}, ${ci_treatment[1]:.2f})")

# 6. Sample Size Calculation
def calculate_sample_size(baseline_cr, target_cr, significance=0.05, power=0.80):
    from scipy.stats import norm
    effect_size = 2 * (np.arcsin(np.sqrt(target_cr)) - np.arcsin(np.sqrt(baseline_cr)))
    z_alpha = norm.ppf(1 - significance/2)
    z_beta = norm.ppf(power)
    n = ((z_alpha + z_beta) / effect_size) ** 2
    return int(np.ceil(n))

sample_size_needed = calculate_sample_size(control_cr, treatment_cr)
print(f"\nSample Size Analysis:")
print(f"Baseline CR: {control_cr:.4f}")
print(f"Target CR: {treatment_cr:.4f}")
print(f"Required per group: {sample_size_needed:,}")
print(f"Actual per group: {len(df[df['group'] == 'Control']):,}")

# 7. Sequential Testing / Running Analysis
fig, axes = plt.subplots(2, 2, figsize=(14, 8))

# Cumulative conversion rates
control_cumsum = df[df['group'] == 'Control']['converted'].cumsum()
treatment_cumsum = df[df['group'] == 'Treatment']['converted'].cumsum()
control_n = np.arange(1, len(control_cumsum) + 1)
treatment_n = np.arange(1, len(treatment_cumsum) + 1)

axes[0, 0].plot(control_n, control_cumsum / control_n, label='Control', alpha=0.7)
axes[0, 0].plot(treatment_n, treatment_cumsum / treatment_n, label='Treatment', alpha=0.7)
axes[0, 0].set_xlabel('Sample Size')
axes[0, 0].set_ylabel('Conversion Rate')
axes[0, 0].set_title('Conversion Rate Over Time')
axes[0, 0].legend()
axes[0, 0].grid(True, alpha=0.3)

# Distribution comparison
axes[0, 1].hist(control_revenue, bins=50, alpha=0.5, label='Control', density=True)
axes[0, 1].hist(treatment_revenue, bins=50, alpha=0.5, label='Treatment', density=True)
axes[0, 1].set_xlabel('Revenue')
axes[0, 1].set_ylabel('Density')
axes[0, 1].set_title('Revenue Distribution')
axes[0, 1].legend()

# Box plot comparison
data_box = [control_revenue, treatment_revenue]
axes[1, 0].boxplot(data_box, labels=['Control', 'Treatment'])
axes[1, 0].set_ylabel('Revenue')
axes[1, 0].set_title('Revenue Distribution (Box Plot)')
axes[1, 0].grid(True, alpha=0.3, axis='y')

# Conversion comparison
conversion_data = pd.DataFrame({
    'Group': ['Control', 'Treatment'],
    'Converted': [control_conversions.sum(), treatment_conversions.sum()],
    'Not Converted': [len(control_conversions) - control_conversions.sum(),
                      len(treatment_conversions) - treatment_conversions.sum()],
})
conversion_data.set_index('Group')[['Converted', 'Not Converted']].plot(
    kind='bar', ax=axes[1, 1], color=['green', 'red'], edgecolor='black'
)
axes[1, 1].set_title('Conversion Comparison')
axes[1, 1].set_ylabel('Count')
axes[1, 1].legend(title='Status')

plt.tight_layout()
plt.show()

# 8. Bayesian Perspective
print("\n8. Bayesian Analysis (informative):")
from scipy.stats import beta

# Assume prior Beta(1, 1) - uninformative
control_successes = control_conversions.sum()
control_failures = len(control_conversions) - control_successes
treatment_successes = treatment_conversions.sum()
treatment_failures = len(treatment_conversions) - treatment_successes

# Posterior distributions
posterior_control = beta(1 + control_successes, 1 + control_failures)
posterior_treatment = beta(1 + treatment_successes, 1 + treatment_failures)

samples_control = posterior_control.rvs(10000)
samples_treatment = posterior_treatment.rvs(10000)

prob_treatment_better = (samples_treatment > samples_control).mean()
print(f"Probability Treatment > Control: {prob_treatment_better:.4f}")

# Visualization
fig, ax = plt.subplots(figsize=(10, 5))
ax.hist(samples_control, bins=50, alpha=0.5, label='Control', density=True)
ax.hist(samples_treatment, bins=50, alpha=0.5, label='Treatment', density=True)
ax.set_xlabel('Conversion Rate')
ax.set_ylabel('Density')
ax.set_title('Bayesian Posterior Distributions')
ax.legend()
ax.grid(True, alpha=0.3)
plt.show()

# 9. Summary Report
print("\n" + "="*50)
print("A/B TEST SUMMARY REPORT")
print("="*50)
print(f"Metric: Conversion Rate")
print(f"Control CR: {control_cr*100:.2f}%")
print(f"Treatment CR: {treatment_cr*100:.2f}%")
print(f"Lift: {lift:.2f}%")
print(f"P-value: {p_value:.4f}")
print(f"Result: {'REJECT H0 - Significant Difference' if p_value < 0.05 else 'FAIL TO REJECT H0 - No Significant Difference'}")
print(f"Winner: {f'Treatment (+{lift:.2f}%)' if p_value < 0.05 and lift > 0 else 'Control (No clear winner)'}")
print("="*50)

Sample Size Determination

• Baseline conversion rate: Current performance
• Target effect size: Minimum detectable difference
• Significance level (α): Usually 0.05
• Power (1-β): Usually 0.80 or 0.90

Key Metrics

• Conversion Rate: Proportion of successes
• Revenue Per User: Average transaction value
• Click-through Rate: Ad performance
• Engagement: Feature adoption

Deliverables

• Test design document
• Sample size calculations
• Statistical test results
• Effect size measurements
• Confidence intervals
• Visualization of results
• Executive summary with recommendation