A/B Test Sample Size Calculator
Calculate the minimum sample size needed for statistically significant A/B test results. Avoid inconclusive tests and make confident optimization decisions.
Test Parameters
Your current conversion rate
Smallest change you want to detect
To calculate test duration
Required Sample Size
Enter your baseline conversion rate to calculate required sample size
Understanding A/B Test Sample Size
Running A/B tests without proper sample size calculation is like flying blind. Here's everything you need to know to run statistically significant tests.
Key Concepts Explained
Confidence Level
The probability that your result is not due to random chance. 95% means you can be 95% confident your results are real.
Statistical Power
The probability of detecting an effect if it really exists. 80% power means you'll catch real improvements 80% of the time.
Minimum Detectable Effect
The smallest improvement you care about detecting. Smaller effects require larger sample sizes to detect reliably.
Sample Size
The number of visitors needed per variation to achieve your desired confidence level and statistical power.
Common A/B Testing Mistakes
- ❌Stopping tests early when results look good
- ❌Running tests without calculating sample size first
- ❌Testing too many variations simultaneously
- ❌Not accounting for external factors and seasonality
Best Practices for A/B Testing
- ✅Calculate sample size before starting any test
- ✅Run tests for full business cycles (include weekends)
- ✅Test one element at a time for clear insights
- ✅Document all test results, even inconclusive ones