Question 995 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
Which TWO statements about handling missing data during EDA are correct? (Select TWO.)
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
✓
If data are missing completely at random (MCAR), listwise deletion yields unbiased estimates.
Options C and E are correct. Option C is correct because when data are Missing Completely at Random (MCAR), the missingness is independent of both observed and unobserved data, so listwise deletion (removing rows with missing values) does not introduce bias; the remaining sample is still a random subsample. Option E is correct because the median is not influenced by extreme values, making it a more robust imputation method compared to the mean, which can be skewed by outliers. Option A is incorrect because dropping columns with >50% missing values is not always recommended; it depends on the importance of the variable and the analysis goals. Option B is incorrect because mean imputation reduces the variance of the imputed variable, as it forces imputed values to the center. Option D is incorrect because Multiple Imputation by Chained Equations (MICE) is not always the safest; it assumes data are Missing at Random (MAR) and can be complex or inappropriate for other missingness mechanisms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dropping columns with >50% missing values is always recommended.
Why it's wrong here
Sometimes columns with high missingness still contain useful information.
- ✗
Mean imputation preserves the variance of the original distribution.
Why it's wrong here
Mean imputation reduces variance.
- ✓
If data are missing completely at random (MCAR), listwise deletion yields unbiased estimates.
Why this is correct
Under MCAR, missingness is independent of data, so deletion is unbiased.
- ✗
Multiple imputation (MICE) is always the safest method regardless of missing data mechanism.
Why it's wrong here
MICE is robust but not always safest; depends on MCAR assumption.
- ✓
Imputing with the median is more robust to outliers than imputing with the mean.
Why this is correct
Median is less affected by outliers.
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Last reviewed: Jun 20, 2026
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