The article explores ways to identify important data points when fitting a logistic regression model. It adapts existing methods from linear regression to this scenario and applies them to a binary logistic regression model. By analyzing the Cook's Distance metric, the researchers determine which observations have a big impact on the model's results. They test their approach using simulations and real data, finding it useful for detecting influential points in logistic regression.