A/B Test Power Analysis Calculator
Calculate the statistical power of your A/B tests to ensure reliable results and avoid wasting resources on underpowered experiments.
What is Statistical Power?
Statistical power is the probability of detecting a true effect in your A/B test when one exists. It's crucial for:
- Avoiding False Negatives: Higher power reduces the risk of missing real improvements
- Resource Optimization: Ensures you run tests long enough to be conclusive
- Decision Confidence: Helps make data-driven decisions with greater certainty
How to Use This Calculator
- Enter your Baseline Conversion Rate (example: enter 10 for 10%)
- Specify the Minimum Detectable Effect you want to identify (example: enter 20 for 20%)
- Choose your desired Statistical Power (recommended: 80%)
- Set your Confidence Level (recommended: 95%)
- Select your Traffic Split between control and variant
- Input your Daily Traffic to estimate test duration
Understanding the Results
The calculator provides:
- Power Curve: Shows how statistical power increases with sample size
- Required Sample Size: Minimum samples needed per variant
- Test Duration: Estimated days to complete the test
- Visual Analysis: Interactive chart for exploring different scenarios
Common Mistakes to Avoid
- Underpowered Tests: Running tests with power below 80%
- Premature Stopping: Ending tests before reaching required sample size
- Ignoring Effect Size: Not considering practical significance
- Traffic Miscalculation: Not accounting for traffic fluctuations
Tips for Better A/B Tests
- Set Realistic Goals: Choose effect sizes based on business impact
- Plan for Seasonality: Account for traffic variations
- Monitor Test Health: Track sample size accumulation
- Document Assumptions: Record your power analysis parameters