A/B Test Significance Calculator

Get A/B Test Results Quickly

To find out which version of a webpage or app performs better in terms of user engagement, conversion rates, or other metrics, A/B testing compares two versions of the same. But careful statistical analysis is needed to determine whether the observed differences are statistically significant, and this tool helps with that.

Users are given the ability to indicate the level of confidence they would like to have in their test findings, which gives them the power to make well-informed decisions. To accommodate various experimental designs and hypotheses, users have the option to select between one-tailed and two-tailed tests.

How To Use?

Here are the steps that can help you understand each terms and the process:



Number of visitors in control group:

This is the total number of users who were exposed to the control version of your test (e.g., the original webpage design).



Number of conversions in control group:

This is the number of users from the control group who completed the desired action (e.g., clicked a button, made a purchase).



Number of visitors in test group:

This is the total number of users who were exposed to the test version of your test (e.g., a new webpage design).



Number of conversions in test group:

This is the number of users from the test group who completed the desired action.



Test type:

This can be either “one-tailed” or “two-tailed”. A one-tailed test looks for a differenceonly in one way (e.g., the test version is better than the control), whereas a two-tailed test seeks to find a difference in both directions (e.g., the test version is either better or worse than the control).



Desired confidence level (%):

This is the statistical confidence level you want for your test. A common choice is 95%, which means if you ran the test 100 times, you would expect the same result 95 times.



Calculate Significance:

Lastly, press the button to view the outcomes. After that, the tool computes the z-score, estimates the conversion rates for the test and control groups, and, depending on the test type and desired confidence level, assesses if the result is statistically significant.

The z-score is a measure of how many standard deviations an element is from the mean. In this context, it’s used to compare the test and control conversion rates.

Benefits of A/B Testing

Absolutely! Manual calculations for A/B test significance can indeed be tedious and error-prone. Thankfully, this free tool automates the process, saving users precious time and resources.

By providing accurate statistical analysis, users can confidently interpret their A/B test results, gaining more precise insights into the effectiveness of various variations and strategies.

The A/B Test Significance Calculator is a crucial tool for running strong A/B tests. It helps users make informed decisions based on data, enhance testing accuracy, and optimize their digital experiences for maximum impact.

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