Variance Reduction in A/B Testing: 5 Techniques to Increase Experiment Sensitivity

In experimentation, achieving significant results on a mature product is challenging due to the small effects most changes have on target metrics, as shown by 1,450 experiments on Microsoft's Bing. Such small effects are difficult for standard A/B tests to detect, often leaving results undecided by evidence. To enhance experiment sensitivity and detect these small effects without waiting for larger samples, practitioners can use variance reduction techniques, which include metric choice, winsorization, CUPED, post-stratification, and triggered analysis. Each technique addresses different aspects of variance, such as choosing metrics closer to the treatment for clarity, capping outliers to prevent dominant effects, using pre-experiment data to adjust outcomes, stratifying users to manage group differences, and filtering analyses to focus on exposed users. GrowthBook implements these techniques to streamline the experimentation process and reduce variance, empowering businesses to make quicker, more informed decisions.

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