DA0-002 Data Governance Practice Question
A data analyst is creating a report on customer satisfaction scores across different regions. The analyst wants to highlight regions that are significantly below average. Which of the following statistical methods is most appropriate for identifying these outliers?
⚠ Common exam trap
Test-takers frequently choose a bar chart with an average line (Option A) because it visually shows deviations, but it lacks a formal statistical criterion to define 'significantly below average,' which the IQR-based box plot provides.
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 with interquartile range (IQR) to identify outliers.
A box plot with interquartile range (IQR) is the most appropriate method because it explicitly identifies outliers as data points falling below Q1 - 1.5*IQR or above Q3 + 1.5*IQR. This directly addresses the analyst's goal of highlighting regions significantly below average, as the IQR method is a standard statistical technique for detecting extreme values in a distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bar chart with average line.
Why it's wrong here
Incorrect. Bar charts show values but do not statistically define outliers.
- ✗
Pie chart of satisfaction categories.
Why it's wrong here
Incorrect. Pie charts show proportions, not outlier detection.
- ✓
Box plot with interquartile range (IQR) to identify outliers.
Why this is correct
Correct. IQR-based box plots are a standard method for identifying statistical outliers.
- ✗
Scatter plot of satisfaction vs. region.
Why it's wrong here
Incorrect. Scatter plots are for relationships, not outlier identification in a single variable.
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