MLS-C01 Exploratory Data Analysis Practice Question

Which TWO of the following are appropriate techniques for detecting outliers in a univariate continuous dataset? (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

Options A and B are correct. Z-score method flags points beyond a threshold (e.g., 3 standard deviations) from the mean. IQR-based outlier detection identifies points beyond 1.5*IQR from the quartiles. Option C is wrong because box plots visualize outliers but are not a detection technique per se; they rely on IQR. Option D is wrong because Pearson correlation is bivariate and not used for univariate outlier detection. Option E is wrong because K-means clustering is typically used for multivariate data and not a standard univariate outlier detection method.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Z-score method
Why this is correct
Z-scores beyond a threshold (e.g., 3) indicate outliers.
✓
IQR (Interquartile Range) method
Why this is correct
IQR method flags points outside 1.5*IQR from Q1 and Q3.
✗
Box plot visualization
Why it's wrong here
Box plot is a visualization, not a detection technique; it uses IQR.
✗
Pearson correlation coefficient
Why it's wrong here
Correlation measures relationship between two variables.
✗
K-means clustering
Why it's wrong here
K-means is for clustering, not univariate outlier detection.

Go deeper

MLS-C01 Exploratory Data Analysis Practice Question

Why each option matters

Option-by-option breakdown

Related to this question