The Shocking Truth About ‘Dumb’ Mean Imputation

Source: Dev.to

Most data teams obsess over fancy imputation models.
They quietly destroy the one thing you actually care about: trustworthy signals.

The hidden trap

Imagine a beautiful 3D puzzle.
Now smash it.
Then fill all the gaps with beige Lego bricks.

That’s mean imputation. Your data looks complete, but the structure that mattered is gone.

Testing the imputation

I tested this on a real dataset.
Mean and median imputation actually beat KNN and MICE on prediction accuracy.

On paper, they “worked”. Under the hood, they wrecked correlations between features.

• The model got better.
• The data got worse.

Choosing your imputer

• Pure prediction: You can tolerate some distortion, but document it clearly.
• Insight or causal analysis: Protect correlations first, even if accuracy drops.
• Stakeholder‑driven decisions: Treat imputation as a business decision, not just a technical one.

Conclusion

The truth: there is no “best” imputer. There is only the best imputer for your goal, and most teams never define the goal.

Which do you optimize for in your work: cleaner predictions or more honest relationships in the data?