Introducing stratified sampling for LaunchDarkly Experimentation

Trusting experiment results is crucial when deciding which feature variation to implement, but many experiments suffer from covariate imbalance, where baseline characteristics are unevenly distributed between control and treatment groups, leading to biased outcomes. This imbalance is particularly problematic in randomized experiments with small sample sizes or in B2B environments. For instance, an e-commerce platform might incorrectly conclude that a new checkout flow is ineffective if a few large enterprise customers skew the control group's average revenue. Stratified sampling addresses this issue by ensuring important user attributes are evenly distributed across groups, thus preventing random imbalances from affecting results. It involves defining what balanced means, evaluating candidate randomizations, and selecting the most balanced one, allowing for more reliable data analysis. This technique is especially useful when the user base is small or skewed, certain attributes significantly impact metrics, or there's a need for greater confidence in results before rollouts. LaunchDarkly offers a way to implement stratified sampling by using a CSV file of user data to assign experiment traffic, although it applies only to known users at test creation. By eliminating covariate imbalance, stratified sampling enables faster iteration, fewer false readings, and more informed decision-making.