What is an A/B test calculator?

An A/B test calculator turns raw test results (visitors and conversions per variant) into a statistical verdict: is the difference real, or noise? This one runs a two-proportion z-test, two-tailed, at 95% significance and 80% power by default. It's the standard convention used to run A/B tests on landing pages, calls to action, and email campaigns across major testing platforms.

What counts as a statistically significant result?

A p-value below 0.05 (95% confidence) that the observed difference isn't due to random chance. Below that threshold, the test results are more likely to be real user behavior than statistical noise.

What's the difference between p-value and confidence interval?

The p-value answers whether the difference is likely real. The confidence interval shows the plausible range of the true difference. A wide interval means more uncertainty even at a low p-value.

How does A/B testing work in practice?

A/B testing works by splitting traffic between two variants (control and challenger), measuring how each performs on a target metric, and using statistics to decide whether the difference reflects real user behavior. This calculator handles the statistics half. You still need to design the test, split the traffic, and pick the metric (conversions, bounce rates, click-through on calls to action).

Why does it ask for daily traffic?

To estimate how many more days the test needs at your current traffic rate to reach the required sample size for the effect size you're seeing.

What if my test isn't significant yet?

The calculator estimates the sample size needed at 80% power for the effect size observed so far, plus days-to-significance if you provide daily traffic. Keep running the test rather than making a premature informed decision.

What types of A/B tests can I use this for?

Any two-variant conversion test: landing pages, calls to action, email campaigns, mobile app onboarding, checkout flows, pricing page CTAs, signup form variants. If you can count conversions on control and variant, the calculator handles the stats. For multivariate testing (more than two variants), use a different statistical correction.

Should I stop a test as soon as it becomes significant?

Generally no. Stopping the moment you cross 95% significance inflates false positives (the peeking problem). Decide your sample size upfront and let the test run to that point.

Can I use this for multivariate testing?

No. This is a two-proportion test built for A/B comparisons. Multivariate testing (three or more variants) needs a different statistical correction (e.g. Bonferroni) to control for multiple comparisons.

Can I compare bounce rates or other non-conversion metrics?

Yes, as long as the metric is a rate (conversions / visitors). Bounce rates, click-through rates, and any binary user behavior metric work the same way in a two-proportion z-test.

Does this replace a full experimentation program?

No. It answers one test's significance. Ongoing experimentation programs need sequential testing corrections, CUPED variance reduction, and portfolio-level tracking that a single A/B test calculator can't provide. See the CRO program recipe for what that ongoing program actually looks like end to end.

Does it account for novelty effects or seasonality?

No. The math only evaluates the sample you provide. Real experimentation programs should run tests across full business cycles to avoid novelty or seasonal bias in user experiences.

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