Under what circumstances might 'removal' of outliers lead to biased results?
- When outliers are a result of data duplication
- When outliers are due to data collection errors
- When outliers are extreme but legitimate data points
- When outliers do not significantly impact the analysis
Removing outliers can lead to biased results when the outliers are extreme but legitimate data points, as they could represent important aspects of the phenomenon being studied.
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