When Clean Data Is Actually Dirty
“Cleaning” data is often treated as a harmless preprocessing step. Delete missing rows. Fill gaps with the mean. Move forward. But cleaning is not neutral. It is a modeling decision that can change: The estimand The sampling mechanism The bias–variance trade-off In this episode, we examine the statistical dangers of deletion and simple imputation — and why naïve cleaning can quietly corrupt inference.
