Traditional A/B testing focuses on comparing the mean effects of different treatments, which may not capture the impact on outliers or specific distribution points. GrowthBook introduces quantile testing for Pro and Enterprise customers, offering a way to evaluate how features affect different data percentiles, such as the 99th percentile latency (P99) rather than just the average. Quantile testing involves creating Fact Tables with relevant data like session IDs, user IDs, timestamps, and latency, and setting up quantile metrics for analysis. This method allows for a more nuanced understanding of feature impacts, such as measuring reductions in the worst-case latency scenarios or analyzing revenue changes across various user spending tiers. For instance, a treatment that reduces P99 latency significantly may not only improve the worst latencies but also provide insights into which user subgroups benefit the most, as seen with quantile metrics showing improvements among moderately high and high spenders. Quantile testing thus provides a comprehensive view of data distributions and feature effects, complementing traditional mean-based metrics.