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Free A/B Test Statistical Significance Calculator

Know for certain whether your test results are real — or just random chance. Enter your data and get an instant significance verdict.

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Variant A

Visitors

Conversions

Variant B

Visitors

Conversions

Confidence level95%

Verdict

Significant.

Variant B wins

p-value0.0314

Rate A2.8%

Rate B3.6%

Relative lift+30.4%

Strength of evidence

Very strongStrongMarginalInconclusive

About this tool

Running an A/B test is straightforward. Knowing whether your results actually mean something is the hard part. Statistical significance tells you whether the performance difference between your control (A) and variant (B) is large enough to be real — and not just the result of random variation. Our free A/B Testing Statistical Significance Calculator gives you an instant, clear answer: enter your visitor and conversion data for both variants and the tool tells you whether your result is statistically significant, at what confidence level, and what your p-value is.

Without significance testing, A/B test results are unreliable. A conversion rate of 3.2% vs 2.8% might look like a clear win — but if your sample is too small or the difference too narrow, it could simply be noise. Acting on insignificant results leads to shipping changes that don’t actually improve performance. This calculator removes the ambiguity by applying the chi-squared or z-test for proportions to determine whether the difference you observed is likely to replicate consistently.

Use this calculator for conversion rate optimization (CRO), landing page A/B tests, email subject line tests, pricing experiments, onboarding flow variants, or any test where you’re comparing two outcomes. The only requirement: a clear variant A and variant B with visitors (or impressions) and conversions (or completions) for each.

Key benefits

Instantly determine whether your A/B test results are statistically significant

Works for any conversion experiment — web, email, product, pricing, survey response rates

Choose your confidence level (90%, 95%, or 99%) to match your testing standards

See your p-value alongside a plain-English significance verdict

Avoid costly decisions based on misleading or inconclusive test results

Completely free — no account or credit card required

How it works

Enter your test data

Input the number of visitors (or impressions) and conversions (or completions) for both your control (A) and your variant (B).

Set your confidence level

Choose 90%, 95%, or 99% depending on how certain you need to be before declaring a winner.

Get your result

See instantly whether your test is statistically significant, your exact p-value, and a clear recommendation on what action to take.

Quick answer

To check if an A/B test is statistically significant, enter the number of visitors and conversions for both variants into a significance calculator. The calculator computes the p-value — the probability the observed difference is due to chance. At 95% confidence (p-value below 0.05), the result is statistically significant. A result above 0.05 means the test needs more data. Most A/B tests require at least 1,000 visitors per variant to produce reliable significance results.

A/B Test Significance Calculator — FAQ

What does statistically significant mean in an A/B test?+

A statistically significant A/B test result means the difference in performance between your control and variant is unlikely to have occurred by chance. At 95% confidence, there is less than a 5% probability that the observed difference was random. A significant result gives you confidence that if you implement the winning variant broadly, you are likely to see the same improvement consistently over time.

What is a p-value in A/B testing?+

A p-value is the probability that the difference you observed between variants A and B occurred by chance, assuming there is actually no real difference between them. A p-value of 0.05 means there’s a 5% chance the result is random — which is the standard threshold for declaring statistical significance at 95% confidence. A p-value above 0.05 means your test is not yet significant and you likely need more data before drawing conclusions.

How long should I run an A/B test before checking for significance?+

Run your A/B test for at least one full business cycle — typically 1–2 weeks — before evaluating significance, even if you hit significance earlier. Checking results too early (known as “peeking”) inflates false-positive rates dramatically. Establish a minimum sample size before the test begins, using our Sample Size Calculator, and commit to running the test until that target is reached regardless of interim results.

What sample size do I need for an A/B test?+

Required sample size depends on your baseline conversion rate, the minimum detectable effect (the smallest improvement you care about detecting), and your confidence level. As a practical guide, most landing page or email A/B tests need at least 1,000 visitors per variant for reliable results — more if your baseline conversion rate is very low or the effect you’re trying to detect is small. Use our Sample Size Calculator to determine your exact requirement.

Can this calculator be used for survey A/B tests?+

Yes. If you’re testing two versions of a survey — different question wording, different response scales, different introduction text, different survey lengths, or different email subject lines for survey invitations — this calculator will determine whether the difference in response rate, completion rate, or answer distribution between the two versions is statistically significant.

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