DA0-002 Data Analysis Practice Question
Which data cleaning method involves replacing a missing value with the average of the available values in that column?
⚠ Common exam trap
Test-takers frequently confuse mean imputation with interpolation or forward-fill, since all three 'fill in' missing values — candidates must recognize that only mean imputation uses the column-wide average rather than neighboring values.
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
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Mean imputation
Mean imputation is the data cleaning technique that replaces missing values in a column with the arithmetic mean (average) of the non-missing values in that same column. This preserves the column's central tendency and keeps the overall sample size intact, which is why it is the standard answer when the question specifies 'replacing a missing value with the average of the available values in that column.'
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Mean imputation
Why this is correct
Mean imputation calculates the arithmetic average of a column's non-missing entries and substitutes that value for each gap, satisfying the stem's requirement to replace missing values with the column average. It preserves the column's central tendency, though it shrinks variance and can distort relationships between variables.
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Interpolation
Why it's wrong here
Interpolation estimates missing values from surrounding data points in a sequence, typically for time-series trends, not from the column's overall average. Mean imputation replaces the gap with the arithmetic mean of available values. Interpolation suits ordered data with a discernible trend.
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Listwise deletion
Why it's wrong here
Listwise deletion discards the entire record containing the missing value, reducing sample size rather than substituting anything. Mean imputation is the method that fills gaps with the column average. Listwise deletion suits cases where missingness is rare and random.
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Forward-fill
Why it's wrong here
Forward-fill propagates the last observed value into subsequent gaps, which suits time-series or sequential data where the prior state persists. It does not compute a column average. Mean imputation is the method that substitutes the arithmetic mean of available values.
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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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