What is multiple imputation, and why is a single fill often not enough?
PICTURE THIS: HOW TO EXPLAIN IT
Simple meaning
Multiple imputation draws several completed datasets from a model of the missing values, analyzes each, and pools estimates so uncertainty from missingness is reflected.
WHY — Missing Data instead of guessing?
Why interviewers care about Missing Data:
people who only read docs from people who shipped.
and tied to Data Science work.
Name the idea, why it exists, then one short example.
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:
- 1Multiple imputation draws several
completed datasets from a model of the missing values, analyzes each, and pools estimates so uncertainty from missingness is reflected.
- 2A single imputation treats
filled numbers as known and overstates precision.
- 3It still relies on
MAR-like assumptions unless the imputation model encodes MNAR structure.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
Multiple imputation draws several completed datasets from a model of the missing values, analyzes each, and pools estimates so uncertainty from missingness is reflected. A single imputation treats filled numbers as known and overstates precision.