MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing exploratory data analysis on a dataset with missing values. The dataset contains a column 'income' with 20% missing values. The income distribution is right-skewed. Which imputation method is most appropriate to preserve the skewness?
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 median income
The median is robust to the right-skewed distribution of income. Imputing with the median preserves the skewness and central tendency without being influenced by outliers, unlike the mean which would pull the imputed values toward the tail and reduce skewness. Option A is wrong because the mean is sensitive to outliers and would distort the distribution. Option C is wrong because dropping rows reduces sample size and may bias the dataset. Option D is wrong because the mode is typically used for categorical data and is not meaningful for continuous skewed data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Impute with the mean income
Why it's wrong here
Mean is affected by skewness and would distort the distribution.
- ✓
Impute with the median income
Why this is correct
Median is robust to skewness and preserves the distribution shape.
- ✗
Drop rows with missing income
Why it's wrong here
Reduces sample size and may introduce bias.
- ✗
Impute with the mode income
Why it's wrong here
Mode is suitable for categorical data, not continuous.
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