DA0-002 Visualization and Reporting Practice Question
A data scientist creates a box plot of employee salaries and notices many outliers above the upper whisker. What action should be taken to best understand the salary distribution?
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
✓
Investigate the outliers to determine if they are data entry errors or valid extremes
Investigating outliers is essential to understand the salary distribution. Outliers above the upper whisker may represent legitimate high salaries (e.g., executives) or data entry errors, and understanding their source guides appropriate analysis. Option A is wrong because a histogram does not inherently clarify the nature of outliers. Option B is wrong because blindly removing outliers can distort the true distribution. Option C is wrong because trimming top salaries without investigation loses valuable information and biases the analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace the box plot with a histogram of the salaries
Why it's wrong here
A histogram also shows distribution but may not highlight outliers as distinctly.
- ✗
Remove all outliers to create a more typical box plot
Why it's wrong here
Removing outliers without analysis may discard important information.
- ✗
Trim the top 5% of salaries and recreate the box plot
Why it's wrong here
Trimming can hide the true spread; better to understand outliers first.
- ✓
Investigate the outliers to determine if they are data entry errors or valid extremes
Why this is correct
Outliers should be investigated to ensure data quality and contextual understanding.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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