Which approach fills in missing values using the mean or median of available data?

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Imputation is the process of filling in missing values with estimated numbers so the dataset can be used for analysis or modeling. When you fill gaps with the mean or median, you’re applying simple imputation based on central tendency. Using the mean preserves the overall level of the variable in roughly symmetric data, while the median is more robust when data are skewed or contain outliers. This approach lets you keep all records instead of discarding incomplete ones or trying other unrelated techniques. It’s distinct from methods like encoding for categorical features (which is not about filling numeric gaps), exploratory data analysis (which is about inspecting data rather than imputing it), or treating outliers as a separate issue (which focuses on unusual values, not missing data).