When Clean Data Is Actually Dirty
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When Clean Data Is Actually Dirty
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About this listen
“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.
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