MLS-C01 Exploratory Data Analysis Practice Question
During exploratory data analysis, a data scientist notices that a feature has a highly skewed distribution. Which transformation is most likely to make the distribution approximately 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
✓
Log transformation
Log transformation is commonly used to reduce right skewness and make the distribution approximately normal. Option B (min-max scaling) is incorrect because it does not change the shape of the distribution. Option C (one-hot encoding) is incorrect because it is used for categorical variables, not for transforming continuous skewed data. Option D (standardization) is incorrect because it does not change the shape of the 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.
- ✓
Log transformation
Why this is correct
Log transformation reduces right skewness and makes the distribution approximately normal.
- ✗
Min-max scaling
Why it's wrong here
Min-max scaling does not change the shape of the distribution; it only rescales the range.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is used for categorical variables, not for transforming continuous skewed data.
- ✗
Standardization (z-score)
Why it's wrong here
Standardization does not change the shape of the distribution; it only centers and scales the data.
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