An interaction effect (also called test collision or experiment interference) happens when two experiments run at the same time on overlapping audiences, and the effect of one test changes depending on which variant of the other test a user receives.
If Test A changes the hero headline and Test B changes the checkout button, a user could experience Variant A's headline with Variant B's button — a combination that was never explicitly designed or intended. If these changes reinforce or counteract each other, both tests will report distorted results.
When Interaction Effects Are a Problem
Not all simultaneous tests interact. Two tests on different pages with non-overlapping audiences are typically safe to run concurrently. Interaction effects become a concern when:
- Tests affect the same page or user flow
- Tests target the same audience segment
- One test changes a global element (navigation, banner, cart) visible throughout the site
- The metric of one test is influenced by the feature being changed in another
A Simple Example
| Experiment | Change |
|---|---|
| Test A | Changes hero CTA copy |
| Test B | Adds urgency banner above the hero |
If the urgency banner reduces attention to the hero CTA (interaction), Test A's results are contaminated. Users in Test A / Variant B may behave differently not because of the copy change alone, but because the banner modified how they perceive the hero section.
How to Manage Interaction Effects
Mutual exclusion — Assign users to only one test at a time using exclusion groups. Ensures clean, uncontaminated results, but reduces the speed of your testing program.
Full factorial design — Run all combinations explicitly (A×B testing) and measure the interaction term directly. Requires more traffic but produces complete results.
Audience segmentation — Route different user segments to different tests, ensuring no user participates in both simultaneously.
Sequential testing — Run tests one at a time. Slowest approach, but eliminates interaction risk entirely.
Variance monitoring — Track whether adding a concurrent test shifts the baseline metric of existing tests. A sudden shift suggests interference.
Most experimentation platforms offer mutual exclusion layers or "layers" concepts to manage this automatically.