DA0-002 Data Analysis Practice Question
Which THREE of the following are appropriate methods to handle outliers in a dataset?
⚠ Common exam trap
Candidates often confuse data preprocessing techniques like binning or imputation with outlier handling methods, but binning is for discretization and mean imputation is not robust for outliers, while the correct methods (transformation, removal, capping) directly address outlier impact.
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
✓
Transforming the data using log transformation
Log transformation compresses the scale of data, reducing the impact of extreme values and making the distribution more symmetric. This is a standard technique for handling skewed data where outliers are present, as it preserves the relative order of observations while mitigating outlier influence.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Transforming the data using log transformation
Why this is correct
Transformation can reduce the impact of outliers.
- ✓
Removing the outlier records
Why this is correct
Removal is acceptable if outliers are errors or not representative.
- ✓
Capping the outlier values at a certain percentile
Why this is correct
Capping limits extreme values.
- ✗
Binning continuous variables
Why it's wrong here
Binning is a discretization method, not specifically for outlier handling.
- ✗
Imputing outliers with the mean
Why it's wrong here
Mean is sensitive to outliers; median is better.
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