MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset with missing values in 3 of 20 features. The missing rate is 5% for each feature. The scientist wants to preserve as much data as possible while avoiding bias. Which imputation strategy is most appropriate?
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 missing values with the median of each feature.
Median imputation (Option D) is the most appropriate because it preserves the dataset size, is robust to outliers, and avoids bias introduced by more complex methods. Removing rows (Option A) would discard approximately 14% of the data if missing patterns are independent, unnecessarily reducing sample size. Mean imputation (Option B) is sensitive to outliers, which could skew the distribution. KNN imputation (Option C) may introduce bias if the neighborhood size is not properly tuned and is computationally expensive for large datasets. Therefore, median imputation provides a simple, robust solution that maintains data integrity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove rows with any missing values.
Why it's wrong here
Removing rows with any missing values discards up to 14% of the data (since 3 features each missing 5% independently), which contradicts the goal of preserving as much data as possible.
- ✗
Impute missing values with the mean of each feature.
Why it's wrong here
Mean imputation can be influenced by outliers, potentially biasing the central tendency and distribution of the feature.
- ✗
Use K-Nearest Neighbors (KNN) imputation.
Why it's wrong here
KNN imputation risks introducing bias when missingness is not completely at random (MCAR), as the 5% missing rate per feature may still correlate with underlying data patterns, distorting the distribution. It is tempting because KNN preserves data structure by using similarity-based estimates, making it ideal for datasets with strong local correlations and MCAR missingness, where such bias is minimised.
- ✓
Impute missing values with the median of each feature.
Why this is correct
Median imputation is robust to outliers, preserves the dataset size, and is a simple, effective method for low missing rates (5% per feature).
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