DA0-002 Data Analysis Practice Question
A data analyst is cleaning a dataset with missing values. Which TWO of the following are acceptable methods for handling missing numerical 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
✓
Mean imputation
Mean imputation and deletion (listwise) are common methods. Mode imputation is for categorical, and forward-fill is for time series; min-max is normalisation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Min-max normalisation
Why it's wrong here
Normalisation is scaling, not handling missing values.
- ✗
Forward-fill
Why it's wrong here
Forward-fill is specific to time series, not a general method.
- ✗
Mode imputation
Why it's wrong here
Mode is for categorical data, not typically for numerical.
- ✓
Mean imputation
Why this is correct
Correct: Replacing missing with mean is acceptable.
- ✓
Deletion of rows with missing values
Why this is correct
Correct: Listwise deletion is acceptable if missing is random and not too many.
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