Understanding mutually exclusive experiments

What are mutually exclusive experiments?

Mutually exclusive experiments are designed to prevent overlap in participant allocation. This means each user is included in only one experiment at a time. By doing so, you avoid the risk of users being exposed to multiple tests simultaneously, which can complicate results. This practice provides more accurate insights and a better understanding of each test's impact.

To set up mutually exclusive experiments, you need to carefully plan your participant allocation. This involves creating rules and mechanisms to ensure that users only enter one experiment. For example:

• Audience segmentation: Define segments based on specific criteria like geography or user behavior.

• Traffic splitting: Use targeting rules to distribute users evenly across experiments without overlap.

By implementing these strategies, you can manage multiple tests effectively. This isolation helps you gain clear, actionable insights from each experiment, improving your decision-making process.

Why use mutually exclusive experiments?

Benefits of implementing mutually exclusive experiments

Mutually exclusive experiments reduce the risk of interaction effects. This makes your data cleaner and more reliable. By isolating variables, you gain clearer insights.

This approach helps manage similar tests targeting the same user segments. It prevents cross-contamination, ensuring each test's results are distinct. You can run multiple experiments without worrying about overlapping influences.

For example, consider two experiments: Experiment A tests a new color for a "Buy Now" button, and Experiment B tests a new placement for the same button. If the experiments are run separately, they might both show positive results. However, if they are run simultaneously, the combination of the new color and new placement might confuse users and lead to negative results. This illustrates the importance of avoiding interaction effects.

For a more detailed guide on how to set up and manage experiments, refer to the Statsig experimentation program documentation. To learn more about working with experiments and best practices, visit the Statsig working with experiments guide.

Practical examples of mutually exclusive experiments

Example scenarios

UI Testing: You can test different sections of a website's UI separately. This avoids any overlap in user groups. It ensures accurate results for each test. Learn more about UI Testing.

Backend vs Frontend Changes: Conduct backend performance tests and frontend design tests independently. This prevents interference between the two. Each test gets clear, isolated results. Explore how to manage interaction effects.

Feature Rollouts: Roll out new features to distinct user groups. This helps measure engagement accurately. It prevents cross-contamination between different user segments. Discover more about sequential testing and A/B/n testing.