What statistical test is used?

A two-proportion z-test: it pools the conversion rates from both variants to estimate the standard error, then computes a z-score and two-tailed p-value. This is the standard approach for binary-conversion A/B tests.

What does 'statistically significant' mean?

It means the observed difference is unlikely to be due to random chance at your chosen confidence level. At 95% confidence, there's only a 5% chance you'd see a difference this large if the variants were truly equal.

What confidence level should I use?

95% is the industry standard for marketing experiments. Use 99% for high-stakes decisions (e.g. pricing changes), and 90% only for low-risk exploratory tests where you accept more false positives.

How many visitors do I need?

Use the Sample Size Calculator to find out before launching. As a rule of thumb, you need enough visitors that the test has statistical power — usually thousands per variant for small effect sizes.

What if my p-value is just above 0.05?

That's a 'borderline' result. Don't declare a winner yet — collect more data. Stopping a test the moment it crosses significance is a common mistake that inflates false-positive rates.

A/B Test Calculator

Compute conversion rates, lift, z-score, and p-value for A/B test results.

100% private — no uploads Free & unlimited Read tutorial

How to use A/B Test Calculator

Follow these steps — everything happens instantly in your browser.

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Step 1. Enter visitors and conversions for your control (Variant A).
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Step 2. Enter the same for your challenger (Variant B).
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Step 3. Pick a confidence level: 90%, 95%, or 99%.
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Step 4. Read the verdict: significant or not, plus lift, z-score, and p-value.

Why use this tool

Frequently asked questions

Everything you might want to know about a/b test calculator.

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