What is the purpose of imputation in data analysis?

Imputation in data analysis primarily serves the purpose of addressing the issue of missing data. When datasets have gaps or missing values, it can lead to biased or incomplete analysis. The process of imputation involves replacing these missing values with substitute values based on certain methods or algorithms, which allows for a more comprehensive dataset.

For instance, common imputation methods include using the mean, median, or mode of the available data to fill in the missing values, or applying more complex techniques like predictive modeling. This ensures that statistical analyses can be performed without the risk of skewed results due to unaccounted-for missing data points. In summary, imputation is critical for enhancing the quality and usability of datasets, allowing analysts to conduct more robust analyses by minimizing the impact of missing information.