Moderate Missing Data Question 114 of 220

When is median imputation better than mean imputation?

Data Science track · Speak this in 60–90 seconds · Faridabad & Delhi NCR

PICTURE THIS: 1, 2, 2, 8

Mean3.25average

Median2middle

Mode2most often

Simple meaning

Median imputation is more robust on skewed numeric columns because a few giants do not dominate the fill value.

WHY — Missing Data instead of guessing?

Why interviewers care about Missing Data:

They are checking judgment

on Missing Data.

A good answer names

the situation, the default choice, and one exception - that reads as experience.

Stay structured

Name the idea, why it exists, then one short example.

Close cleanly

End with when you use it and one common pitfall.

STEPS — What happens step by step?

Before you speak the answer, walk the interviewer through these steps:

1
Median imputation is more
robust on skewed numeric columns because a few giants do not dominate the fill value.
2
Both methods still shrink
variance and ignore relationships with other features.
3
They are acceptable baselines,
not a complete missing-data strategy for MNAR survey fields.
4
Give an example
One tiny concrete case you can say aloud.
5
Common mistake
What juniors usually get wrong.
6
Close
When you pick this over the alternative.

EXAMPLE — See it in action

Here's a short line you can speak, broken into clear beats:

Say this line

“Both methods still shrink variance and ignore relationships with other features.”

Break into beats

Bothmethodsstillshrinkvarianceand

Speaking order

2987408337471632900

Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.

Key takeaway

Median imputation is more robust on skewed numeric columns because a few giants do not dominate the fill value. Both methods still shrink variance and ignore relationships with other features.