Question 1,605 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
During EDA, a data scientist notices that a numeric feature 'age' has values ranging from 0 to 150, but expects adult ages between 18-100. Which TWO steps should the scientist take to investigate?
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
✓
Compute summary statistics (min, max, percentiles)
Computing summary statistics (min, max, percentiles) helps identify the range and potential outliers in the 'age' feature. Option E is correct because a box plot visualizes the distribution and clearly shows outliers, allowing the data scientist to investigate further. Option A is incorrect because removing rows with age > 100 without understanding the context may discard valid data (e.g., errors or special cases). Option C is incorrect because log transformation changes the scale but does not help in identifying outliers; it is used to handle skewed distributions. Option D is incorrect because imputing age values outside 18-100 with the mean would distort the distribution and is not appropriate for investigating outliers; it should only be considered after understanding the nature of the 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.
- ✗
Remove all rows with age > 100
Why it's wrong here
Incorrect because removing rows without understanding context may discard valid data.
- ✓
Compute summary statistics (min, max, percentiles)
Why this is correct
Correct because it helps identify the range and potential outliers.
- ✗
Apply log transformation to normalize the distribution
Why it's wrong here
Incorrect because log transformation does not help in identifying outliers; it is used for skewed distributions.
- ✗
Impute age values outside 18-100 with the mean
Why it's wrong here
Incorrect because imputing with mean would distort the distribution and is not appropriate for investigating outliers.
- ✓
Create a box plot to visualize outliers
Why this is correct
Correct because a box plot visualizes the distribution and clearly shows outliers.
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Last reviewed: Jun 20, 2026
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