Imputation is a statistical technique used to fill in missing data points in a dataset. When researchers collect data, they may encounter gaps due to various reasons, such as non-responses in surveys or equipment malfunctions. Imputation helps maintain the integrity of the dataset by estimating these missing values based on the available information, allowing for more accurate analysis. 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 reliability of their findings and make better-informed decisions based on complete datasets.