A/B Test Sample Size Calculator
Compute sample size + days-to-significance for conversion rate experiments
At 5% baseline conversion, detecting a 10% relative improvement (i.e. 5.00% → 5.50%) at 95% significance with 80% power needs 31,234 visitors in each variant, or 62,468 total.
How to use
- Enter baseline + MDE
- Set significance and power
- Review sample size and days
Use cases
- Growth marketing
- Product experimentation
- Conversion optimization
About the A/B Test Sample Size Calculator
Running an A/B test without computing sample size first is how teams end up with 'inconclusive' results that waste months. This calculator uses the exact formula for two-proportion z-tests to tell you upfront: how many visitors per variant, and how long you'll wait. Fix your MDE first, plan the test, then ship.
Features
- Inputs: baseline rate, minimum detectable effect, significance level (α), power, daily traffic
- Outputs: per-variant sample, total sample, days to significance
- Uses inverse normal CDF for exact z-score derivation
- Plain-English scenario summary
How it works
- Enter your current (baseline) conversion rate.
- Enter the minimum lift you'd care about (MDE).
- Keep default significance (5%) and power (80%) or adjust.
- Enter your daily visitors to see days-to-significance.
Frequently asked questions
Why is my sample size so huge?
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Because detecting small improvements at low baselines is statistically hard. At 1% baseline, detecting a 5% relative lift (1.00% → 1.05%) needs ~60k per variant. The smaller the effect, the exponentially more data you need.
What values should I use for α and power?
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Industry standard: α=5% (accept a 5% false-positive rate) and power=80% (detect 80% of real effects). More rigorous teams use α=1% and power=90% but sample sizes balloon.
What's a 'minimum detectable effect'?
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The smallest improvement you'd care about. If a 2% lift isn't worth shipping, set MDE=5% and you'll stop the test much sooner. Honest teams set this BEFORE running, not after seeing results.
Should I use a Bayesian approach instead?
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Fixed-sample frequentist (what this tool computes) is still industry norm — easy to explain to stakeholders, works with most A/B tools. Bayesian is better if you can stop early based on peeking; requires specialized tooling.
Why does the calculator say 'infinity' days?
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Your daily traffic is 0 or the required sample exceeds your traffic × reasonable run length. Either increase traffic, relax your MDE, or pick bigger pages to test.