MLS-C01 Exploratory Data Analysis Practice Question
A data scientist uses Amazon QuickSight to visualize a dataset and observes that a numerical feature has a skewness of 2.5 and a kurtosis of 8. Which transformation should they apply to make the distribution more normal?
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
✓
Apply a log transformation.
Apply a log transformation. A skewness of 2.5 indicates a strong right skew (positive skew), and a kurtosis of 8 indicates heavy tails (leptokurtic). Log transformation is effective in reducing right skewness and making the distribution more symmetric, which is a common step toward normality. Option A (Z-score normalization) standardizes the data but does not change the shape of the distribution. Option B (Box-Cox with lambda=0.5) is a square root transformation, which is less effective than log for high skewness; Box-Cox typically requires choosing an optimal lambda, and lambda=0 would be a log transform. Option C (Min-Max scaling) rescales the range but does not affect skewness or kurtosis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Standardize the feature using Z-score normalization.
Why it's wrong here
Standardization does not change the shape of the distribution.
- ✗
Apply a Box-Cox transformation with lambda=0.5.
Why it's wrong here
Box-Cox with lambda=0.5 is a square root transformation, less effective for high skewness.
- ✗
Apply Min-Max scaling to the range [0,1].
Why it's wrong here
Min-Max scaling does not change distribution shape.
- ✓
Apply a log transformation.
Why this is correct
Log transformation reduces right skewness.
Quick reference
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