DA0-002 Data Analysis Practice Question
A data analyst is cleaning a dataset and finds missing values in a categorical variable representing customer region. Which imputation method is most appropriate?
⚠ Common exam trap
A common mix-up: candidates confuse imputation methods across data types, incorrectly applying mean or median imputation to categorical variables because they focus on central tendency without considering data type appropriateness.
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
✓
Mode imputation
Mode imputation is the most appropriate method for a categorical variable because it replaces missing values with the most frequently occurring category, preserving the distribution of the data. Unlike mean or median imputation, which are designed for numerical data, mode imputation maintains the categorical nature of the variable and avoids introducing invalid values. This approach is simple and effective when missing data is random and the category is well-represented.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Drop rows with missing values
Why it's wrong here
Dropping rows may discard valuable data; imputation is preferred when appropriate.
- ✓
Mode imputation
Why this is correct
Mode is appropriate for categorical variables.
- ✗
Mean imputation
Why it's wrong here
Mean is for numeric data.
- ✗
Median imputation
Why it's wrong here
Median is for numeric data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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