When Running Multiple A/B Tests Simultaneously Is Safe and When It Is Not

Experimentation teams often face the dilemma of whether to run overlapping A/B tests or isolate them, and the answer depends on how the tests interact. By default, Optimizely assigns users to experiments using independent hashing, meaning participation in one test does not influence bucketing in another, making overlap statistically safe in most cases. Interaction effects become a real risk when two tests share the same page, the same user funnel, or the same conversion metric, as one experiment can distort the results of the other in ways neither team can detect independently. A practical example is a homepage discount test running alongside a checkout redesign that removes a promo-code field, where the combination causes abnormal drop-offs that falsely make the redesign appear to underperform. Optimizely recommends using exclusion groups to enforce mutual exclusivity only when such conflict conditions are identified, since unnecessary isolation splits traffic and slows down both experiments.