Assessing the Impact of Outlier Removal on Statistical Inference and Data Distribution Integrity Across Studies

Posted: Jan 20, 2013

• Harper White
• Samuel Martin
• Isabella Green

Abstract

The practice of outlier removal represents one of the most common yet controversial preprocessing steps in statistical analysis across scientific disciplines. Outliers, typically defined as observations that deviate markedly from other members of the sample, present analytical challenges that span theoretical, methodological, and practical dimensions. While conventional statistical wisdom often advocates for the identification and removal of extreme values to enhance model fit and improve inferential properties, the consequences of such procedures for data distribution integrity remain inadequately characterized. This investigation addresses the critical gap in understanding how outlier removal procedures systematically alter distributional properties and impact statistical inference across diverse research contexts.