DA0-002 Data Analysis Practice Question
A data analyst is cleaning a dataset and finds that 5% of values in the 'income' column are missing. The analyst decides to impute missing values using the mean of the non-missing values. Which potential issue should the analyst be most concerned about?
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 imputation may reduce the variance and distort the distribution.
Mean imputation reduces variance and can distort relationships, especially if data is skewed. It may also bias estimates if missingness is not random.
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 may reduce the variance and distort the distribution.
Why this is correct
Mean imputation pulls values toward the mean, reducing variance and potentially biasing results.
- ✗
The imputation is not valid because the missing rate is too low.
Why it's wrong here
5% is acceptable for mean imputation, but other issues persist.
- ✗
The imputation will increase the standard deviation of the variable.
Why it's wrong here
Mean imputation typically reduces variance.
- ✗
The imputation will create outliers.
Why it's wrong here
Mean imputation does not create outliers; it may mask them.
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