“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.