Outliers can be true (natural variations) or false (errors). True outliers should be retained, while false outliers may need to be removed or corrected.
Types of Outliers
Outliers can skew statistical results, affecting measures of central tendency and variance. Proper handling is crucial for accurate data interpretation.
Options include retaining the outlier if it's a true value, assigning a new value if it's an error, or removing it if it significantly impacts the analysis.
Handling Outliers
Large datasets can mitigate the impact of outliers, but small datasets may require robust statistical tests to handle them effectively.