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A/B test sample size calculator
Before you start a test, know how many visitors it needs and how long that takes. Enter your baseline conversion rate and the smallest change worth detecting.
Your test
How the control converts today.
The smallest change worth knowing about: 5.00% → 5.50%.
All versions together.
Smaller effects cost a lot more traffic
- 2% lift752,703· 753 d
- 5% lift122,124· 123 d
- 10% lift31,234· 32 d
- 15% lift14,193· 15 d
- 20% lift8,158· 9 d
- 30% lift3,780· 4 d
- 50% lift1,471· 2 d
Visitors per version for each effect size, and days at your traffic. Halving the effect you want to detect roughly quadruples the visitors you need.
How it works
The calculator uses the standard two-sided test for the difference between two proportions. You choose how sure you want to be about a winner (confidence) and how likely you want to be to spot a real effect of the size you care about (power). It returns the visitors each version needs and, from your daily traffic, how many days the test will run.
The trade-off that matters
Sample size grows with the square of how precise you want to be. That is why the panel on the right shows the cost of smaller effects: if a 5% lift would take months, the honest options are to test something bolder, test where traffic is higher, or learn another way.
Good habits
- Write down the hypothesis and the sample size before you start.
- Run whole weeks, so weekdays and weekends are both in it.
- Don’t stop early on a significant peek, and don’t extend a test that didn’t win.
- Test the riskiest assumption behind an idea, not the idea’s polish.
Outcomify
Test the assumptions behind an idea, before you build it
An A/B test is one way to learn. Outcomify maps the assumptions under each solution on your Opportunity Solution Tree, so you test the riskiest first and keep the evidence next to the decision. Free plan, no credit card.