MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with missing values in 30% of the rows for the 'age' column. The data scientist decides to impute the missing values with the median of the observed 'age' values. What is a potential drawback of this approach?
⚠ Common exam trap
The MLS-C01 exam often tests the subtle distinction between bias (which is a general risk of any imputation under non-random missingness) and variance reduction (which is a specific, guaranteed statistical consequence of constant-value imputation).
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
✓
The imputed values may reduce the variance of the 'age' distribution.
Imputing missing values with the median of the observed data artificially concentrates imputed values around the center of the distribution. This reduces the overall variance of the 'age' column because the imputed values do not reflect the natural spread of the data, potentially distorting downstream analyses like regression or clustering that rely on variance structure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The imputation will introduce bias if the missing values are not random.
Why it's wrong here
This is a potential drawback, but the question asks for a drawback of median imputation specifically; the more direct drawback is reduced variance.
- ✗
Imputation using median is computationally expensive for large datasets.
Why it's wrong here
Median imputation is computationally cheap.
- ✓
The imputed values may reduce the variance of the 'age' distribution.
Why this is correct
Replacing missing values with a constant reduces the variability of the feature.
- ✗
The imputed values will increase the variance of the feature, leading to overfitting.
Why it's wrong here
Median imputation does not increase variance.
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