You can now use stratified sampling in LaunchDarkly Experimentation to reduce covariate imbalance and improve the reliability of experiment results. Instead of relying solely on random assignment, LaunchDarkly evaluates many candidate randomizations and selects the one that produces the most balanced control and treatment groups based on customer-provided attributes.

This helps teams avoid misleading results when a small number of high-impact users—such as large accounts—skew outcomes, and provides a stronger statistical foundation before an experiment begins.

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