DA0-002 Data Analysis Practice Question
A data analyst notices that a dataset of customer ages has several missing values. Which method for handling missing data is most appropriate if the data is missing completely at random and the analyst wants to preserve sample size?
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
✓
Impute with the mean age
Mean imputation replaces missing values with the mean, preserving sample size, but can bias estimates. However, for MCAR, it is a common simple approach.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Forward-fill using the previous value
Why it's wrong here
Forward-fill is for time series, not random missing.
- ✓
Impute with the mean age
Why this is correct
Mean imputation is simple and preserves sample size.
- ✗
Replace missing values with zero
Why it's wrong here
Replacing with zero would introduce bias.
- ✗
Delete all rows with missing data
Why it's wrong here
Listwise deletion reduces sample size.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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