Optimize your conversion rates with our A/B testing calculator designed for UX & marketing pros.
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A/B testing, also known as split testing, is a method of comparing two versions of a webpage or user experience against each other to determine which one performs better. Essentially, it is an experiment where two or more variants of a page are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal.
This AB Testing calculator helps users determine whether the difference in performance between the control group (A) and the variation group (B) is statistically significant. This calculator requires users to input a sample size exposed to each variant, and the number of conversions for each. Then select the type of test (1-tailed or 2-tailed) you want to run and the calculator evaluates the likelihood that the observed difference in conversion rates between the control and variation is statistically significant at 95% confidence.
In hypothesis testing, specifically within the context of A/B tests, the choice between a 1-tailed and a 2-tailed test is crucial because it reflects our expectations and the directionality of the test. A 1-tailed test (also known as a one-sided test) is used when we have a specific hypothesis about the direction of the effect; that is, we predict that one group (usually the treatment) will perform better or worse than the other group (usually the control). In this case, we are only interested in finding evidence for an increase or a decrease, not just any difference.
Conversely, a 2-tailed test (or two-sided test) is employed when we do not have a specific prediction regarding which group will perform better. The hypothesis is simply that there is a difference between the two groups, and the test assesses whether the treatment group's conversion rate is either significantly higher or lower than that of the control group. With a 2-tailed test, we are open to finding evidence of a significant effect in either direction.
This distinction affects the interpretation of the test results. In a 1-tailed test, a negative t-statistic (indicating the control group performed better than the treatment group) would not be considered evidence against the hypothesis. In a 2-tailed test, however, this result could still indicate a significant difference between groups, but in the opposite direction to what might have been expected. Decision on which test to use must be made before conducting the A/B test and should not be changed after seeing the results, as this would invalidate the test's statistical assumptions and could lead to incorrect conclusions.
When running an A/B test, several factors should be considered to ensure the results are valid and actionable: