What are the engineering challenges and biases in overlapping A/B tests?

WHAT THIS TESTS: Your grasp of causal inference at scale when multiple experiments target the same users. Interviewers want to know if you understand that simultaneous influences are not necessarily additive and that your data pipeline must preserve the ability to disentangle them.

A GOOD ANSWER COVERS: First, define interaction effects as situations where the simultaneous influence of two variables on an outcome is not additive, per the Optimizely definition. Second, describe the engineering challenge: user-level collision. When two tests affect the same surface, like a Shop Now button, event attribution becomes ambiguous and standard bucketing breaks down because a single user action may be credited to both tests. Third, explain data bias: if you run mutually exclusive tests sequentially and simply sum the lifts, you miss synergy or suppression. For example, a text change yielding plus two percent and a color change yielding plus five percent might combine to plus ten percent, not seven percent, revealing a positive interaction effect. Fourth, propose mitigation strategies. For tightly coupled elements, use multivariate testing instead of overlapping A/B tests because MVT blends variations into combinations and detects interaction effects out of the gate. For loosely coupled tests, implement mutual exclusion layers in your assignment service so users are bucketed into only one test per exclusion group. Fifth, design the analytics pipeline to account for interactions by logging test membership as dimensions on every event, modeling interaction terms in the analysis layer, and using stratified variance estimation rather than assuming independent observations. Sixth, mention that you should predefine interaction hypotheses for tightly coupled surfaces rather than fishing for interactions post hoc, which inflates false discovery rates.

COMMON WRONG ANSWERS: Claiming that running tests on different page sections eliminates all risk. Stating that randomization automatically handles overlap without acknowledging non-additive combinations. Proposing to analyze tests in isolation and add the lifts together. Suggesting MVT for every scenario regardless of traffic constraints or element coupling. Asserting that a simple global holdout group is sufficient to catch all interaction effects without modeling individual combinations.

LIKELY FOLLOW-UPS: How do you handle interaction effects when you have low traffic and cannot run a full MVT? What is your strategy when product teams refuse to coordinate test schedules? How would you detect a negative interaction effect in production data after launch? At what point does the complexity of interaction modeling outweigh the benefit of running concurrent tests?

ONE CONCRETE EXAMPLE: Imagine two overlapping A/B tests on an ecommerce checkout page. Test A changes the submit button text and Test B changes its color. If analyzed independently, the winning text might show a two percent lift and the winning color a five percent lift. A pipeline that simply adds these assumes a seven percent combined lift. However, if the color red amplifies urgency only when paired with action-oriented text, the true combined lift could be ten percent. A well-designed pipeline would either run an MVT upfront for this tightly coupled element or, if overlapping tests are required, would log both test memberships on each click event and include an interaction term in the regression model to capture the extra three percent synergy rather than misattribute it.

Source: optimizely.com