Running Multiple A/B Tests at Once: When Overlap Is Safe

Running multiple A/B tests simultaneously is a common question for experimentation teams. The key is understanding when it's safe to overlap tests and when isolation is necessary. Optimizely recommends avoiding unnecessary isolation as it slows down testing.

The main risk of overlapping experiments is the possibility of interaction effects - when exposure to one test changes how a user responds to another. This can distort results, making it seem like a test performed poorly when it might have actually performed well in combination with another test.

Key factors that increase interaction risk are if tests are on the same page, use the same conversion metric, or target the same user flow. In these cases, tests should either be combined into one with more variations, run sequentially, or placed in mutual exclusion groups.

Mutual exclusion groups split traffic 50/50 between tests so no visitor sees both variations. While this prevents interaction effects, it does increase the amount of traffic needed to reach statistical significance, as each test only receives half the traffic. This is more appropriate when tests are on the same page or measure the same metric.

In most cases, running tests concurrently with minimal isolation is sufficient, as the interaction risk is usually negligible. Optimizely provides tools to configure mutually exclusive experiments when needed. The key is understanding when overlap is safe based on the specifics of each test.