Example Concept: To assess the impact of missing data, first identify the missing data mechanism: MCAR (Missing Completely at Random), MAR (Missing at Random), or MNAR (Missing Not at Random). Conduct sensitivity analysis by comparing results from different imputation methods (e.g., mean imputation, multiple imputation) and complete case analysis to evaluate how missing data might alter your conclusions. Additionally, use visualization techniques like missingness maps to understand patterns and potential biases.