MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with 1 million rows and 50 features. The scientist wants to detect outliers in a numerical feature 'transaction_amount' which has a long right tail. The scientist suspects that outliers are due to data entry errors and should be removed. Which outlier detection method is MOST robust for this scenario?
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) with multiplier 1.5
The IQR method (Option A) is the most robust for detecting outliers in the 'transaction_amount' feature because it is based on quartiles and does not assume any underlying distribution, making it resistant to skew and extreme values. Z-score (Option C) is inappropriate because it assumes a normal distribution, which the long right tail violates. Mahalanobis distance (Option B) assumes multivariate normality and is not suited for univariate outlier detection. DBSCAN (Option D) is computationally expensive on 1 million rows and is designed for density-based clustering, not for univariate outlier detection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Interquartile range (IQR) with multiplier 1.5
Why this is correct
IQR method is non-parametric and robust to skewness.
- ✗
Mahalanobis distance
Why it's wrong here
Mahalanobis distance assumes multivariate normality and is not robust to skewed distributions.
- ✗
Z-score with threshold 3
Why it's wrong here
Z-score assumes a normal distribution, which is not the case for a long-tailed feature.
- ✗
DBSCAN clustering
Why it's wrong here
DBSCAN is computationally intensive and typically used for multivariate outlier detection.
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