A/A testing


What is A/A testing?

A/A testing uses A/B testing to test two identical versions of a page against each other. Typically, this is done to check that the tool being used to run the experiment is statistically fair. In an A/A test, the tool should report no difference in conversions between the control and variation, if the test is implemented correctly.

Why test identical pages?

In some cases, you may want to monitor on-page conversions where you are running the A/A test to track the number of conversions and determine the baseline conversion rate before beginning an A/B or multivariate test.

In most other cases, the A/A test is a method of double-checking the effectiveness and accuracy of the A/B testing software. You should look to see if the software reports that there is a statistically significant (>95% statistical significance) difference between the control and variation. If the software reports that there is a statistically significant difference, that’s a problem. You will want to check that the software is correctly implemented on your website or mobile app.

Things to keep in mind with A/A testing:

When running an A/A test, it’s important to note that finding a difference in conversion rate between identical test and control pages is always a possibility. This isn’t necessarily a poor reflection on the A/B testing platform, as there is always an element of randomness when it comes to testing.

When running any A/B test, keep in mind that the statistical significance of your results is a probability, not a certainty. Even a statistical significance level of 95% represents a 1 in 20 chance that the results you’re seeing are due to random chance. In most cases, your A/A test should report that the conversion improvement between the control and variation is statistically inconclusive—because the underlying truth is that there isn’t one to find.

Optimizely Stats Engine and A/A testing:

When running an A/A test with Intelligence Cloud, in most cases, you can expect the results from the test to be inconclusive—meaning the conversion difference between identical variations will not reach statistical significance. In fact, the number of A/A tests showing inconclusive results will be at least as high as the significance threshold set in your Project Settings (90% by default).

In some cases, however, you might see that one variation is outperforming another or a winner is declared for one of your goals. The conclusive result of this experiment occurs purely by chance, and should happen in only 10% of cases, if you have set your significance threshold to 90%. If your significance threshold is higher (say 95%), your chances of encountering a conclusive A/A test is even less (5%).