MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with missing values. Which technique is most appropriate for imputing missing values in a numerical feature that follows a normal distribution?
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 is suitable for normally distributed data as it preserves the mean. Median is robust to outliers, not normality. Mode is for categorical data. Standard deviation is not an imputation method. KNN imputation is non-parametric.
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 preserves the mean of the normal distribution.
- ✗
Standard deviation imputation
Why it's wrong here
Standard deviation is not an imputation method.
- ✗
Mode imputation
Why it's wrong here
Mode is used for categorical data.
- ✗
Median imputation
Why it's wrong here
Median is robust to outliers, but not specifically for normal distributions.
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