Assessing the Influence of Outlier Detection Techniques on Statistical Model Robustness and Data Interpretation Accuracy

Posted: Mar 11, 2023

• Grace Flores
• Theodore Moore
• Sarah Young

Abstract

The presence of outliers in datasets represents one of the most persistent challenges in statistical modeling and data analysis. Traditional approaches to outlier detection have primarily focused on identifying and removing anomalous observations to improve model performance metrics. However, the broader implications of these detection methodologies on the robustness of statistical models and, more critically, on the accuracy of data interpretation have received insufficient attention in the literature. This research addresses this gap by systematically examining how different outlier detection techniques influence not only model performance but also the interpretative conclusions drawn from analytical results. Our investigation introduces several novel contributions to the field. First, we propose a comprehensive evaluation framework that simultaneously assesses outlier detection techniques across multiple dimensions of model robustness and interpretative accuracy. Second, we develop the concept of 'interpretative drift' as a quantitative measure of how outlier removal decisions propagate through analytical workflows and influence final conclusions. Third, we provide empirical evidence demonstrating that certain outlier detection approaches can substantially affect analytical outcomes.