Median Imputation
Median imputation is a data preprocessing technique used to handle missing values in datasets by replacing them with the median of the available values for that variable. It is a simple and robust method that is less sensitive to outliers compared to mean imputation, making it suitable for skewed distributions. This technique helps maintain dataset completeness for analysis or machine learning tasks.
Developers should use median imputation when working with datasets containing missing values, especially for numerical variables with skewed distributions or outliers, such as income or house prices. It is commonly applied in data cleaning pipelines for exploratory data analysis, statistical modeling, or machine learning preprocessing to avoid bias from extreme values. This method is particularly useful in fields like finance, healthcare, or social sciences where data may be non-normally distributed.