Free Tool

Bayesian A/B Test Calculator

A Bayesian A/B test calculator turns your raw visitors and conversions into the probability that B beats A, the expected loss of shipping the wrong variant, and 95% credible intervals – not just a p-value. It models each variant as a Beta distribution and gives you a direct, risk-adjusted ship/no-ship answer in seconds. Paste your test results below to get started.

A/B Test Results Enter data to calculate

Enter the raw counts from your testing platform (Optimizely, VWO, Convert, etc.)

Control (A)

Variant (B)

How much prior belief to inject. "Weak" = data-driven; "Strong" = more conservative.

Enter your test data to see whether you should ship or keep testing.

Why Bayesian over Frequentist?

Classical (frequentist) testing gives you a p-value – the probability of seeing your data if there was no effect. That's not what you actually want to know. You want to know: what's the probability that B is better than A?

Bayesian testing answers that directly. It also lets you stop tests early without inflating false-positive rates, and it quantifies the expected loss of making the wrong decision – giving you a risk-adjusted framework for shipping winners. For the full breakdown, read our guide on Bayesian vs. frequentist A/B testing.

Prefer a classic p-value, or planning a test before you run it? Try the statistical significance calculator and the A/B test sample size calculator.

How to read the results

Probability to Beat Control

The probability that Variant B has a higher true conversion rate than Control A. Above 95% is typically a strong signal to ship.

Expected Loss

If you ship the winning variant and you're wrong, how much conversion rate do you expect to lose? Below 0.5% is considered safe to ship.

Relative Uplift

The observed percentage improvement in conversion rate. Remember: observed uplift ≠ true uplift. Use this alongside probability-to-beat-control.

Want someone to interpret your test results?

Book a free strategy call. Our CRO team will review your test data and tell you exactly what to do next.

Embed this calculator on your site

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Go deeper
A/B TestingBayesian vs Frequentist A/B TestingBayesian vs Frequentist A/B testing explained — when to use each, how they handle early stopping, and which tools support them.
A/B TestingA/B Testing Statistical Significance ExplainedWhat statistical significance actually means in A/B testing, why 95% is the standard, and the mistakes that lead to false positives and premature test calls.
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A/B TestingHow Much Does A/B Testing Cost?The real cost of A/B testing — tools, team time, opportunity cost, and agency fees. How to calculate whether testing is worth it for your traffic level.