MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with missing values in a numeric column. The missing rate is 30% and the data is not missing completely at random. Which imputation method should the data scientist avoid to minimize bias?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Mean imputation
Mean imputation (Option A) should be avoided when data is not missing completely at random (NMAR) because it can introduce bias by underestimating variance and distorting the relationships between variables. Options B (model-based imputation), C (k-NN imputation), and D (multiple imputation) are more robust for non-random missing data as they account for patterns in the data and produce less biased estimates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Mean imputation
Why this is correct
Mean imputation can introduce bias and reduce variance, especially when data is not missing completely at random.
- ✗
Model-based imputation using linear regression
Why it's wrong here
Model-based imputation can be used if the model is correctly specified.
- ✗
k-Nearest Neighbors imputation
Why it's wrong here
k-NN imputation can be less biased than mean imputation but may still have issues with high missing rates.
- ✗
Multiple imputation using chained equations
Why it's wrong here
Multiple imputation is appropriate for non-random missing data as it accounts for uncertainty.
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