When should you use A/A testing?
Again, A/A testing is primarily used to ensure the accuracy of an A/B testing tool. There are plenty of tools out there, but you can't know if they're accurate unless you run A/A tests.
The three instances when you might use an A/A test include when using a new testing tool, establishing a baseline conversion rate, and setting your minimum sample size to reach statistical significance.
Determining a testing platform is working correctly
When you use a new A/B testing platform, you must ensure its accuracy. A/A testing can help you determine if a new tool you've never used before actually works correctly. If there's a significant difference in test results, it may indicate issues with the software.
With A/B testing, you want the results to yield a clear winner. However, you don't want a clear winner with A/A testing because it means there's a discrepancy in the test data since the two variations are identical. If you find a significant difference in the data, it could mean that the tool is being used incorrectly or that it's inaccurate.
Establishing a baseline conversion rate for a page
Before running an A/B test, you should know the baseline conversion rate that helps you understand which variation performs best. By comparing two identical variations of a campaign, you can determine the expected conversion rate before changes are made.
As long as the results are accurate and there are no statistically significant differences in the data once the experiment is complete, you should have a general understanding of your baseline conversion rate that represents the expected campaign conversion rate without any changes.
Setting a sample size for A/B testing
A/A testing requires a larger minimum sample size than A/B testing, but it can help you set a sample size for your tests. By examining data from testing two identical variants, you can observe the variability for estimating the required sample size for A/B tests.
For instance, if the A/B testing tool yields significantly different results for identical variants in A/A testing, it may mean your sample size isn't large enough. A small sample size may not be sufficient enough to measure your KPIs and determine if an A/B test is truly accurate, meaning you might miss out on opportunities that could impact results.
On the other hand, a larger sample size gives you the most accurate data.
Flaws of A/A Testing
A/A testing can help businesses ensure the accuracy of the A/B tools they use and how they use them while determining sample size and establishing baseline conversion rates. Unfortunately, no testing method is perfect.
A/A testing assumes that two identical pages or campaigns should produce similar results, but this isn't always the case because various factors can affect test accuracy. For instance, one variant might perform better than the other, but that doesn't necessarily mean the tool is ineffective.
A/A testing also requires a larger sample size than A/B testing because you're testing two identical versions. In addition, using an A/A test to determine a conversion rate benchmark doesn't necessarily mean your A/B tests will yield the same results. Even if you've set up your experiment perfectly, external factors like buyer preferences, consumer behaviors, and market conditions can affect conversion rates during A/B testing.