MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset and wants to identify outliers in a numerical feature. The feature is not normally distributed. Which technique is robust to non-normal distributions?
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
✓
Use the IQR method: flag values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR.
The IQR method, because it does not assume a normal distribution and uses quartiles to identify outliers. Option A (MAD) is robust but compares deviations from the median; however, the IQR method is more commonly used for non-normal data. Option C (Z-score) assumes normality. Option D (flagging values more than 3 standard deviations from the mean) also assumes normality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compute the Median Absolute Deviation (MAD) and flag values with MAD > 3.
Why it's wrong here
MAD is robust but less commonly used for outlier detection in EDA.
- ✓
Use the IQR method: flag values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR.
Why this is correct
Does not assume normality; uses robust quartiles.
- ✗
Calculate the Z-score and flag values with |Z| > 3.
Why it's wrong here
Assumes normal distribution.
- ✗
Flag values more than 3 standard deviations from the mean.
Why it's wrong here
Assumes normality and is sensitive to outliers.
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Same concept, more angles
2 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist wants to identify outliers in a dataset with 1,000 samples and 5 numerical features. Which technique is most appropriate for univariate outlier detection?
easy- A.Principal component analysis (PCA)
- ✓ B.Interquartile range (IQR) method
- C.Mahalanobis distance
- D.Z-score with a threshold of 3
Why B: The IQR method, where outliers are defined as points below Q1 - 1.5*IQR or above Q3 + 1.5*IQR, is appropriate for univariate outlier detection as it does not assume a specific distribution and is robust to extreme values. PCA (A) is a dimensionality reduction technique, not for outlier detection. Mahalanobis distance (C) is for multivariate outliers. Z-score with threshold 3 (D) assumes normality and is sensitive to extreme outliers.
Variation 2. A data scientist wants to identify outliers in a dataset. Which TWO techniques are commonly used for outlier detection during EDA?
easy- ✓ A.Box plot
- B.Heatmap
- ✓ C.Z-score analysis
- D.Bar chart
- E.Pearson correlation coefficient
Why A: 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.
JA
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