How to Avoid False Positives in High-Velocity Experimentation

A/B testing often results in higher than expected false positive rates, even at companies with advanced experimentation programs like Microsoft and Airbnb, where rates can range from 6% to 26%. This occurs when tests are run too quickly or improperly, leading to incorrect conclusions about the effectiveness of changes. The primary causes of false positives include premature peeking at results, testing multiple variables simultaneously, sample ratio mismatches, and underpowered tests. These issues can lead to significant inefficiencies and resource wastage, as teams may mistakenly interpret random variations as meaningful improvements. To mitigate such errors, best practices include pre-committing to sample sizes and test durations, using A/A tests for calibration, applying sequential testing to manage peeking, checking for sample ratio mismatches, and employing multiple testing corrections like Holm-Bonferroni and Benjamini-Hochberg. Additionally, validating results through causal chains and applying variance reduction techniques such as CUPED can further reduce false positives. These strategies are part of the comprehensive approach offered by GrowthBook, which integrates various features to ensure more reliable and accurate experimentation outcomes.

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