This paper advocates for using blocked (stratified) assignment in online A/B tests to handle highly skewed population data. While blocking yields only modest precision gains (5-10%) due to the limits of discretization, the authors demonstrate it offers two crucial structural benefits over post-hoc statistical adjustments. First, fixed-weight blocking correctly targets the true average treatment effect, avoiding the severe bias introduced by efficiency-weighted alternatives. Second, blocking localizes extreme outliers, enabling targeted within-block winsorization that effectively controls noise without destroying the tail-end treatment effect.
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