A/B Test Analysis Calculator

How It Works

1. Enter your control group data (original version):
   • Total number of visitors
   • Number of conversions
2. Enter your variant group data (new version):
   • Total number of visitors
   • Number of conversions
3. Set your desired significance level (default 95%)
4. Get comprehensive statistical analysis of your test results

Understanding the Results

The calculator provides:

• Statistical significance indicator
• Conversion rates for both variants
• Absolute and relative improvement
• P-value and Z-score
• Confidence intervals

Statistical Parameters

• Statistical Significance: Determined by comparing p-value to significance level
• P-value: Probability of seeing such results by chance
• Z-score: Number of standard deviations from the mean
• Standard Error: Expected variation in the difference between conversion rates
• Confidence Interval: Range where the true difference likely lies

Common Use Cases

1. Website Optimization:

   • Landing page changes
   • Call-to-action modifications
   • Design updates

2. Pricing Tests:

   • Price point optimization
   • Discount strategies
   • Package comparisons

3. Feature Launches:

   • New feature validation
   • UI/UX improvements
   • Content strategy testing

Best Practices

1. Sample Size Requirements:

   • Ensure sufficient sample size before analysis
   • Use our A/B Test Sample Size Calculator
   • Don't stop tests early based on preliminary results

2. Test Duration:

   • Run tests for complete business cycles
   • Account for weekday/weekend variations
   • Use our A/B Test Duration Calculator

3. Result Interpretation:

   • Consider practical significance alongside statistical significance
   • Look at both relative and absolute improvements
   • Account for confidence intervals in decision making

Limitations

• Assumes normal distribution
• Best for binary outcomes (conversion/no-conversion)
• Doesn't account for seasonal variations
• Single metric analysis only

Tips for Success

1. Plan Ahead:

   • Calculate required sample size before starting
   • Set test duration based on traffic volume
   • Define success metrics in advance

2. Monitor Health:

   • Check for test validity regularly
   • Look for data collection issues
   • Verify even traffic distribution

3. Make Decisions:

   • Consider business impact alongside statistics
   • Document learnings for future tests
   • Plan follow-up experiments

Related Calculators

A/B Test Sample Size Calculator

Before running your test, use this calculator to determine how many visitors you need for statistically significant results.

A/B Test Duration Calculator

Calculate how long you need to run your test to reach the required sample size based on your daily traffic.