A/B Test Sample Size Calculator
Find out how many visitors you need per variation to reach statistical significance. Based on the Z-test for two proportions.
Test Parameters
Your current conversion rate
Relative improvement to detect (e.g. 20% = % → %)
Probability the result is not due to chance
Probability of detecting a real effect
To estimate test duration
Required Sample Size
Per Variation
visitors needed
Total (Both Variations)
visitors needed
Estimated Duration
at visitors/day
enter daily visitors above
Baseline Rate
Target Rate (to detect)
Effect Size Comparison
How sample size changes with different minimum detectable effects at your current settings.
| MDE | Target Rate | Per Variation | Total Visitors | Days |
|---|---|---|---|---|
The Formula
This calculator uses the Z-test for two proportions, the standard method for determining sample size in A/B tests comparing two conversion rates.
n = (Zα/2 + Zβ)2 × [p1(1 - p1) + p2(1 - p2)] / (p2 - p1)2
n = required sample size per variation
p1 = baseline conversion rate (your current rate)
p2 = expected conversion rate after the change (p1 × (1 + MDE))
Zα/2 = Z-score for the confidence level (two-tailed)
90% confidence → Z = 1.645
95% confidence → Z = 1.960
99% confidence → Z = 2.576
Zβ = Z-score for the statistical power
80% power → Z = 0.842
90% power → Z = 1.282
Source: Chow, S., Shao, J., & Wang, H. (2008). Sample Size Calculations in Clinical Research, 2nd Edition. Chapman & Hall/CRC Biostatistics Series. Also described in: Wikipedia — Sample size determination (Proportions) and Evan Miller's A/B Testing Sample Size Calculator.
Need a better baseline to test against?
A higher baseline conversion rate means smaller sample sizes and faster tests. Build landing pages that convert from the start.
Try Punapai FreeFrequently Asked Questions
How many visitors do I need for an A/B test?
It depends on your baseline conversion rate, the minimum effect you want to detect, your confidence level, and statistical power. A typical test with a 5% baseline, 20% minimum detectable effect, 95% confidence, and 80% power needs around 1,530 visitors per variation (3,060 total).
What is statistical significance in A/B testing?
Statistical significance means the difference between your two variations is unlikely to be caused by random chance. A 95% confidence level means there is only a 5% probability the observed difference happened by accident. This is the industry standard for A/B tests.
What formula is used to calculate A/B test sample size?
The standard formula is based on the Z-test for two proportions: n = (Zα/2 + Zβ)2 × [p1(1-p1) + p2(1-p2)] / (p2-p1)2. This is the same formula used by tools like Optimizely, VWO, and Evan Miller's calculator.
What is the minimum detectable effect (MDE)?
The minimum detectable effect is the smallest relative improvement over your baseline that you want to be able to detect. For example, if your baseline is 5% and your MDE is 20%, you are looking to detect a change to at least 6%. Smaller MDEs require larger sample sizes because the difference is harder to distinguish from noise.