Multiple imputation is a statistical technique used to handle missing data in datasets. Instead of simply ignoring or filling in missing values with a single estimate, it creates several different plausible datasets by replacing missing values with estimates based on the observed data. This approach helps to reflect the uncertainty associated with the missing information. After generating these multiple datasets, statistical analyses are performed on each one. The results are then combined to produce overall estimates and standard errors. This method improves the accuracy and reliability of the conclusions drawn from the data, making it a valuable tool in research and data analysis.