Imputation is a statistical technique used to fill in missing values in a dataset. This is important because missing data can bias the results of analysis and reduce the statistical power of a study. Imputation methods involve estimating and replacing missing values based on patterns and relationships observed in the rest of the dataset. There are various imputation techniques available, such as mean imputation, hot-deck imputation, and multiple imputation, each with its own advantages and limitations. Imputation is a crucial step in data preprocessing and analysis, particularly in fields such as epidemiology, social sciences, and economics.