Median imputation is best used when dealing with continuous data that is not normally distributed, as the median is less affected by outliers compared to the mean. It is particularly useful in large datasets where the presence of missing values can significantly impact the results of statistical analyses. Examples include patient vital signs, laboratory results, and other clinical measurements.
