MLS-C01 Exploratory Data Analysis Practice Question
A data scientist wants to identify outliers in a dataset. Which TWO techniques are commonly used for outlier detection during EDA?
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
✓
Box plot
Box plots (A) visually identify outliers as points beyond the whiskers (typically 1.5×IQR). Z-score analysis (C) flags data points with an absolute Z-score greater than 3, indicating they are far from the mean. Heatmaps (B) show correlations between variables, not outliers. Pearson correlation (E) measures linear relationships, not outliers. Bar charts (D) display categorical frequencies and do not detect 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.
- ✓
Box plot
Why this is correct
Box plots show outliers as points outside the whiskers.
- ✗
Heatmap
Why it's wrong here
Heatmap shows correlation values, not outliers.
- ✓
Z-score analysis
Why this is correct
Z-scores above 3 or below -3 are often considered outliers.
- ✗
Bar chart
Why it's wrong here
Bar charts are for categorical data, not outlier detection.
- ✗
Pearson correlation coefficient
Why it's wrong here
Correlation measures relationship, not outliers.
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