This article explains how to address missing data through Multiple Imputation under MAR and MNAR mechanisms. It walks through building practical models, shows why MI produces more reliable estimates than Complete-Case analysis, and highlights how properly handling missingness preserves the validity of your conclusions.

This article explores the impact of missing data by breaking down the three main mechanisms—MCAR, MAR, and MNAR. It explains how these mechanisms influence your analysis and the risks of dropping data units with missing values. By understanding these concepts you can minimize bias and uncertainty, ensuring reliable predictions and insights.