DA0-002 Data Analysis Practice Question
A dataset contains a variable 'Income' with many missing values. The analyst decides to impute missing values with the median income of the non-missing values. Which type of imputation is this?
⚠ Common exam trap
The trap is confusing median imputation with other imputation methods like interpolation or forward-fill. Candidates might also think deletion is a form of imputation, but it is not.
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
✓
Median imputation
Imputing missing values with the median of the non-missing values is exactly median imputation. It is a common method for handling missing data, especially when the distribution is skewed or contains outliers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Interpolation
Why it's wrong here
Interpolation estimates missing values from neighbouring data points along an ordered sequence, such as time series. It would suit ordered observations with trend continuity, but the stem replaces every missing income with one constant median, which uses no neighbouring values.
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Deletion
Why it's wrong here
Deletion removes records or variables containing missing values rather than substituting them. It would be chosen when missingness is minimal and dropping rows is acceptable, but here the analyst replaces each missing income with the median of observed values, which is substitution, not removal.
- ✓
Median imputation
Why this is correct
Median imputation replaces each missing entry with the median calculated from the observed, non-missing incomes, satisfying the stem's requirement to use the median of non-missing values. Unlike mean imputation, the median resists distortion from outliers and skew, making it robust for income data, which is typically right-skewed.
- ✗
Forward-fill imputation
Why it's wrong here
Forward-fill carries the last observed value forward into subsequent missing positions, preserving order dependence. It would be chosen for sequential or time-ordered data, but the stem substitutes a single computed median for all missing entries, ignoring position entirely.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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