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On the Benefit of Blocking for Online Experiments with Skewed Data
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 s
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