Question 315 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with missing values in several columns. The dataset contains both numerical and categorical features. Which approach should the data scientist use to handle missing values while minimizing bias and preserving relationships in the data?
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 (e.g., MICE) to impute missing values
Multiple Imputation by Chained Equations (MICE) models each missing value as a function of other variables, preserving relationships and reducing bias. Option B (forward-fill) is unsuitable for non-time-series data and can introduce bias. Option C (deleting rows) reduces sample size and may introduce bias if data is not missing completely at random. Option D (mean/median imputation) distorts distributions and reduces variance, potentially biasing 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.
- ✓
Use multiple imputation (e.g., MICE) to impute missing values
Why this is correct
MICE models each variable as a function of others, preserving relationships and reducing bias.
- ✗
Use forward-fill to propagate the last observed value
Why it's wrong here
Forward-fill is appropriate for time series but not for general datasets.
- ✗
Delete all rows with missing values
Why it's wrong here
Listwise deletion can introduce bias if missingness is not completely random.
- ✗
Replace missing values with the mean or median of each column
Why it's wrong here
Mean/median imputation reduces variance and ignores relationships between variables.
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Last reviewed: Jun 20, 2026
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