Topic: Why Single Imputation Fails

Worked Variance Comparisons Under Mean Imputation

Why One Random Imputation Still Hides Uncertainty

The Law of Total Variance in Regression Imputation

Single Imputation and Missing-Value Uncertainty

Standard Error Underestimation From a Single Completed Dataset

Stochastic Regression Imputation and Marginal Variance

Pairwise Imputation Patterns and Inconsistent Correlations

Mean Imputation’s Point Mass and Distribution Shape

Hot-Deck Imputation and Repeated Donor Values

Deriving the Variance After Mean Imputation