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A/B Test Calculator

Plan, run and analyze split tests with confidence. Statistical significance, sample size and test duration — all in one free, browser-based tool.

Test data

Enter visitors and conversions for each variant. Variant A is the control. You can add up to 5 variants.


Test settings

How many visitors per month will see the page after you roll out the winner.

Results

Verdict
Add data to see results
Enter visitors and conversions for at least two variants to calculate statistical significance.
Best variant
Observed lift
P-value (frequentist)
Probability winner is better (Bayesian)
Beta(1,1) prior, normal approximation

Test parameters

Plan how many visitors per variant you need before starting your test.

Your current conversion rate for the variant you want to beat.
Smallest relative lift you want to detect. A 10% lift on a 3% baseline means detecting a 3.3% conversion rate.

Sample size needed

Visitors needed per variant
Total visitors needed
Conversions per variant
Detecting
Absolute lift to detect

Traffic & expectations

Estimate how many days you need to run your test based on your current traffic.

Total daily visitors that will be split across variants.

How long to run the test

Estimated duration
Days needed
Weeks needed
Visitors per variant
Total visitors
Recommendation: Run your test for full weekly cycles (multiples of 7 days) to capture weekday/weekend variation. Avoid stopping the test early or peeking at results before reaching the calculated sample size.

Frequently asked questions

Is the A/B test calculator free?

Yes. The calculator is completely free, with no sign-up and no usage limits. Run as many significance, sample size and duration calculations as you need.

Does my test data stay private?

Yes. Every calculation runs entirely in your browser. The visitors and conversions you enter are never uploaded or stored on a server.

What is the difference between the frequentist and Bayesian results?

The frequentist p-value is the probability of seeing a difference this large if the variants were actually identical. The Bayesian figure is the probability that the winning variant is genuinely better, which is often easier to act on.

What does statistical significance mean here?

A result is significant when the p-value falls below your chosen threshold, for example 0.05 at 95% confidence. It means the observed lift is unlikely to be down to random chance, not that the lift is large or permanent.

How are sample size and test duration calculated?

Both use your baseline conversion rate, the minimum detectable effect, the confidence level and the statistical power to find the visitors per variant needed. Duration simply divides that target by your daily traffic.

How long should I run an A/B test?

Run the test until you reach the calculated sample size, and cover full weekly cycles to capture weekday and weekend behaviour. Avoid stopping early or peeking at results, as that inflates false positives.

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