Imputation is a statistical technique used to fill in missing data within a dataset. When researchers collect information, they may encounter gaps due to various reasons, such as non-responses in surveys or data entry errors. Imputation helps maintain the integrity of the dataset by estimating and replacing these missing values based on available information. There are several methods of imputation, including mean imputation, where the average of existing values is used, and more complex techniques like multiple imputation or k-nearest neighbors. By using imputation, analysts can improve the accuracy of their analyses and make more informed decisions based on complete datasets.