If your A/A test looks faulty, don’t panic. It’s actually a normal part of the process. Random variation can cause temporary differences between your groups, even though they should perform the same. The key is to let the test run long enough to reach statistical significance. If you see big, consistent differences early on, that usually points to a setup issue (like uneven traffic splits, tracking errors, or duplicate users being counted). If it is a setup issue we will spot this fairly quick and correct the error. That means we will double-check your implementation, then rerun the test. A properly configured A/A test should eventually show no meaningful difference between the groups.