Accuracy: Provides more accurate estimates than simply removing or averaging the missing data. Completeness: Maintains the integrity of the dataset, allowing for more comprehensive analysis. Bias Reduction: Reduces the likelihood of bias that can occur when missing data is ignored. Improved Decision-Making: Enhances the quality of decisions based on the imputed data, leading to better patient outcomes.
