The article discusses how detecting outliers and analyzing residuals in logistic regression can be challenging due to differences from linear regression. In logistic regression, the probability of success depends on the explanatory variables, leading to unique residual distributions. This means that claims about standardized residuals being normally distributed are not appropriate in logistic regression with binary data. The study highlights the need for careful consideration when detecting outliers in logistic regression and emphasizes the importance of understanding the unique characteristics of residual distributions in this type of analysis.