Optimizing A/B Testing Calendar Using Design of Experiments

When conducting A/B testing to evaluate new features or changes, testing each factor individually can be time-consuming and resource-intensive. To optimize the testing pipeline and reduce the number of experiments needed, we can leverage concepts from the field of design of experiments (DOE), which allows us to efficiently test multiple variables simultaneously while still gaining valuable insights.

Traditional A/B Testing vs. Screening Experiments

In a basic A/B test, we have a control group and a test group, and we run experiments feature by feature to isolate the effect of each individual factor. However, this approach can be slow, especially when there are many features to evaluate.

Instead of focusing solely on statistical significance, we can use screening experiments to quickly identify the most important features to prioritize for full A/B testing. These experiments test multiple variables at once, providing directional insights on which factors have the greatest impact.

Design of Experiments (DOE)

DOE is a systematic approach to designing efficient experiments that test multiple factors concurrently. By carefully selecting test cases, DOE allows us to estimate main effects and low-order interactions while minimizing the number of runs required. Techniques like orthogonal arrays, fractional factorial designs, and Plackett-Burman designs leverage…