DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is performing EDA on a dataset with numerical features. Which methods are appropriate for identifying outliers? (Select TWO).
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
✓
Z-score method
IQR method uses Q1 - 1.5*IQR and Q3 + 1.5*IQR to define outliers. Z-score method uses threshold (e.g., |Z| > 3) to identify 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.
- ✗
Mean imputation
Why it's wrong here
Imputation is for handling missing values, not outlier detection.
- ✗
Pearson correlation coefficient
Why it's wrong here
Correlation measures relationship, not outliers.
- ✓
Z-score method
Why this is correct
Points with |Z| > 3 are often considered outliers.
- ✗
Standard deviation alone
Why it's wrong here
Standard deviation alone doesn't identify outliers; Z-score uses it.
- ✓
Interquartile range (IQR) method
Why this is correct
Outliers are points below Q1 - 1.5*IQR or above Q3 + 1.5*IQR.
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