Data imputation

Data imputation is a statistical technique used to fill in missing values in a dataset. When researchers or analysts encounter gaps in their data—perhaps due to errors or incomplete responses—they can use imputation methods to estimate what those missing values might be. This helps maintain the integrity of the dataset, allowing for more accurate analysis and insights. Common methods include using the average of existing values, predicting values based on other data points, or utilizing machine learning algorithms. Ultimately, imputation allows for better decision-making and understanding by providing a more complete picture of the information available.