Living with outliers
How to detect extreme observations in data analysis
Palabras clave:
outliers, extreme cases, atypical observations, outlier detection, outlier treatmentResumen
This paper provides a practical guide to identifying outliers. We outline five statistical methods specifically designed to spot extreme observations: (1) standardized scores, (2) interquartile range, (3) standardized residuals, (4) Cook's Distance, and (5) Mahalanobis's Distance. To enhance the learning experience, we share both raw data and R scripts, empowering researchers to apply these techniques to their own data. Outliers are often viewed with skepticism by data analysts due to their potential adverse effects such as violating assumptions, hindering visualizations, leading to biased estimates, and altering the direction of coefficients. By following the procedures outlined in this paper, scholars in a variety of fields can make substantial progress in the quality of their data analysis.
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