With over 130+ pre-built integrations and flexible APIs, you can easily centralize data from across your tech stack
Make the most out of your data and unlock powerful growth marketing possibilities with these other top marketing tools.
Build any custom integration with our open, flexible APIs that are simple to use and implement.
Check out apps that have been stealing all the spotlight.
Email and SMS marketing insights, ecommerce resources, and the latest Omnisend news
Featuring insights and analysis into all aspects of DTC ecommerce
Educational video and live training to help you make the most out of Omnisend.
Successful email & SMS strategies that you can copy for your own store.
Vous pouvez parcourir notre site Internet en français, ou continuer en anglais, en cliquant ci-dessous. Nous vous demanderons également de répondre à quelques questions pour nous aider à améliorer votre expérience.
We’d like to understand why the local language is not a good fit for you.
We’d like to understand why the local language was not a good fit for you.
Comment trouvez-vous notre site en français ?
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.
A two-sided test accounts for the possibility that your variant could have a negative impact on your result.
The level of confidence you can have that your results are not due to random chance.
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.
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.
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.
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.
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.
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 shows the exact probability that the outcome of your A/B test is a result of chance.
0.01571
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.
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.
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).
Check out these free tools we designed to help you save time and simplify your work: