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Randomization in experiments ensures validity through independence between treatment and covariates, not through guaranteed balance. While the Central Limit Theorem shows randomization tends to balance covariates, factors like small sample sizes, extreme distributions, multiple test groups, and many covariates can cause imbalance. Despite this, experiments remain valid because randomization breaks systematic relationships between treatments and confounders. Balance improves precision and reduces sensitivity to covariate specification, but independence—not balance—is what enables valid causal inference. Individual experiments may still reach incorrect conclusions due to chance, similar to hypothesis testing, but the approach itself remains sound.

Randomization Works in Experiments, Even Without Balance

Randomization tends to balance covariates, but there is no guarantee

Causes of covariate imbalances despite randomized assignment

Experiment validity is about independence, not balance