Which data cleansing technique involves filling in missing values in a dataset based on statistical methods?

Imputation is a data cleansing technique that involves filling in missing values in a dataset based on statistical methods such as mean, median, or mode imputation. It helps maintain data integrity and completeness by replacing missing values with estimated values derived from the remaining data. Imputation is commonly used in various domains, including data analysis, machine learning, and business intelligence, to handle missing data effectively and minimize its impact on downstream processes.