AI0-001 AI Models and Data Engineering Practice Question
A data analyst is cleaning a dataset and finds that 20% of the values for the 'age' column are missing. Which imputation method is most robust if the data is not normally distributed?
⚠ Common exam trap
CompTIA often tests the misconception that mean imputation is always the default or best choice for numerical data, but the trap here is that candidates overlook the importance of distribution shape and outlier sensitivity, leading them to select mean imputation despite the data not being normally distributed.
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
✓
Median imputation
Median imputation is the most robust method for handling missing values in the 'age' column when the data is not normally distributed because the median is unaffected by outliers or skewness. Unlike the mean, which is sensitive to extreme values, the median provides a central tendency measure that better represents the typical value in non-normal distributions, preserving the dataset's integrity for downstream modeling.
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 it's wrong here
Mean is affected by outliers.
- ✓
Median imputation
Why this is correct
Median is robust to non-normal distributions.
- ✗
Mode imputation
Why it's wrong here
Mode is for categorical data.
- ✗
Remove rows with missing values
Why it's wrong here
Loses 20% of data.
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