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 customers to input a sample size and number of conversions for each variation. After you have input this data, simply 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.

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:

• Sample Size: Determining the appropriate number of participants needed to detect a statistically significant difference
• Randomization: Randomly assigning users to the control or variant group to minimize selection bias.
• Duration: Running the test for a sufficient duration to ensure a sufficient sample size
• Measureable: Ensure all user experiences are being properly measured using a tool such as Google Analytics
• Segmentation: Considering different user segments, as the effect might be significant for certain groups but not for others.
• Statistical Significance: Choosing the confidence level (typically at 95% or 99%) to determine when the results are statistically significant. This calculator defaults to 95% confidence.