Living with outliers: : How to detect extreme observations in data analysis - Figueiredo Filho, Dalson; Silva, Lucas; Pires, Antônio; Malaquias, Caio (BIB - Revista Brasileira de Informação Bibliográfica em Ciências Sociais, 2023)
Data analysts often view outliers with skepticism due to their potential adverse effects, such as violating assumptions, hindering visualizations, and leading to biased estimates. In this paper, we present a practical guide for identifying outliers, which includes the step-by-step description of five statistical methods specifically designed to detect extreme observations: (1) standardized scores, (2) interquartile range, (3) standardized residuals, (4) Cook’s distance, and (5) Mahalanobis distance. To enhance the learning experience, we provide both raw data and R scripts, empowering researchers to apply these techniques to their own data. By following the procedures outlined in this paper, scholars in a variety of fields will be able to make substantial progress in the quality of their data analysis.