A/A Testing

A/A testing is similar to A/B testing, however you’re testing the same page to ensure that that tool you’re using to run the experiment is statistically fair. The result of an A/A test should be that the 2 pages have no difference regarding conversions.

The Importance Of A/A Testing

While it may seem redundant to test the same page twice, this is important to track how many conversions take place on the page so you have a baseline before starting your A/B tests. This is a way to ensure the software you’re using to A/B test is accurate and to ensure there’s no statistically significant difference in your A/A test (>95%).

You should also note that it is possible to have a difference in conversion when running an A/A test. This doesn't mean you’re using a poor A/B testing platform, as there’s always room for random results when testing. In an A/A test, the variation between the control and varion group should be inconclusive since there should be no big differences with the 2 groups.

Running A/A Tests With Taplytics

You can expect inconclusive results when running an A/A test with Taplytics. This means that the results will not be statistically significant, which is the ideal outcome. Once you’ve completed your A/A test, you can move onto A/B and multivariate testing. You can learn more about A/B testing with Taplytics here.

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