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A/B testing calculator

Calculate the statistical significance of your A/B split conversion test. Test your emails, forms, landing pages, subject lines, and so much more with our easy to use statistical significance calculator.

Visitors
Conversions
Conversion rate
A
Visitors
Conversions
Conversion rate
1.00%
B
Visitors
Conversions
Conversion rate
1.14%
Hypothesis

A two-sided test accounts for the possibility that your variant could have a negative impact on your result.

Confidence

The level of confidence you can have that your results are not due to random chance.

Calculate
Result
No result yet!

The conversion rate for Variant B (1.14%) was 0.14% higher than the conversion rate for Variant A (1.00%). You can be 90% certain that implementing these changes will improve your conversion rate.

Significant result!

The conversion rate for Variant B (x.x%) was y% higher than the conversion rate for Variant A (z.z%). You can be 90% certain that variant B will perform better than variant A.

Significant result!

The conversion rate for Variant B (x.x%) was y% lower than the conversion rate for Variant A (z.z%). You can be 90% certain that variant B will perform worse than variant A.

Result not significant

Variant B’s conversion rate (1.00%) was equal to variant A’s conversion rate (1.00%). You cannot say, with 95% confidence, that variant B will perform better or worse than variant A.

Result not significant

Variant B’s conversion rate (1.70%) was y% higher than the conversion rate for Variant A (1.00%). But you cannot say, with 95% confidence, that variant B will perform better or worse than variant A.

Result not significant

Variant A’s conversion rate (1.70%) was y% higher than the conversion rate for Variant B (1.00%). But you cannot say, with 95% confidence, that variant A will perform better or worse than variant B.

P value

P-value shows the exact probability that the outcome of your A/B test is a result of chance.

0.01571

What is statistical significance?

Statistical significance is a measure used in research to help us figure out if the results of a study are likely real, or if they happened by chance. We can check the reliability of our findings by calculating the probability that the differences in the test results are not just random fluctuations. When something has greater statistical significance, this means that we are more confident in the validity of our results.

When the number that shows up (called the p-value) is small, it means we can be more confident in what we found because it's less likely to be random.

How can you calculate statistical significance?

Calculating statistical significance involves looking at the data and using some math to see if the differences or patterns we see are likely real or just random luck. You can do this math by hand, or you can use our A/B test calculator.

One common way to do this is by calculating a p-value, which tells us the likelihood of getting these results by chance.

If the p-value is very small (usually less than 0.05), it suggests that the results are statistically significant, meaning they're probably not due to random chance.

How to use our free A/B testing calculator

Our A/B test calculator takes care of the math for you, making it easier for you to calculate statistical significance.

Simply enter the results of your A/B test in our calculator above: enter the number of visitors and conversions from the A and B variants from your test.

This statistical significance calculator lets you choose which confidence level you’d like to get the result for (90%, 95%, or 99%).

When you get the test results, you’ll see whether the A/B test results are statistically significant. You’ll also get the p-value (a p-value below 0.05 is good; a p-value below 0.03 is ideal).

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