Missing data can arise from various sources, including non-response in surveys, loss to follow-up in longitudinal studies, or errors in data collection. Ignoring missing data or using simple methods like complete case analysis can lead to significant biases. Imputation methods offer a way to use all available data and improve the robustness of epidemiological studies.
