"Use of Multiple Imputation for the Analysis of Incomplete Data"

"Use of Multiple Imputation for the Analysis of Incomplete Data" is a statistical method that addresses missing information in a dataset. Instead of ignoring or deleting incomplete entries, it creates several plausible versions of the missing data based on available information. These complete datasets are then analyzed separately, and the results are combined to produce more accurate and reliable conclusions. This approach helps reduce bias and improves the validity of research findings when some data points are missing, making it a valuable tool for handling real-world data that often has gaps.