Question 1,727 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with missing values. The missing data mechanism is missing at random (MAR). Which imputation method is most appropriate to preserve relationships between variables?
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
✓
Use multiple imputation by chained equations (MICE).
Multiple imputation by chained equations (MICE) is well-suited for missing at random (MAR) data as it models each variable with missing values conditional on other variables, preserving relationships. Option A (removing rows) reduces sample size and can introduce bias if data are not MCAR. Option B (KNN) assumes data are missing completely at random (MCAR) and may not handle MAR well. Option D (mean imputation) reduces variance and distorts relationships.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all rows with any missing values.
Why it's wrong here
Deletion reduces sample size and may introduce bias if data are MAR.
- ✗
Use k-nearest neighbors imputation.
Why it's wrong here
KNN imputation works best when missing is MCAR, not necessarily MAR.
- ✓
Use multiple imputation by chained equations (MICE).
Why this is correct
MICE models each variable with missing values conditional on others, suitable for MAR.
- ✗
Replace missing values with the mean of the column.
Why it's wrong here
Mean imputation underestimates variance and distorts relationships.
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Last reviewed: Jun 20, 2026
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