MLS-C01 Exploratory Data Analysis Practice Question
A data scientist wants to identify outliers in a dataset with 1,000 samples and 5 numerical features. Which technique is most appropriate for univariate outlier detection?
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
✓
Interquartile range (IQR) method
The IQR method, where outliers are defined as points below Q1 - 1.5*IQR or above Q3 + 1.5*IQR, is appropriate for univariate outlier detection as it does not assume a specific distribution and is robust to extreme values. PCA (A) is a dimensionality reduction technique, not for outlier detection. Mahalanobis distance (C) is for multivariate outliers. Z-score with threshold 3 (D) assumes normality and is sensitive to extreme outliers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA is not an outlier detection method.
- ✓
Interquartile range (IQR) method
Why this is correct
IQR is robust and suitable for univariate outlier detection.
- ✗
Mahalanobis distance
Why it's wrong here
Mahalanobis distance is for multivariate outliers.
- ✗
Z-score with a threshold of 3
Why it's wrong here
Z-score assumes normality and is not robust.
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