In epidemiology, data integrity is paramount for drawing accurate conclusions about disease patterns, risk factors, and treatment outcomes. If missing data are MCAR, the absence of data points does not bias the analysis, allowing researchers to use techniques like listwise deletion without significant risk of introducing bias. However, if the data are not MCAR, specialized imputation methods are required to handle the missing data appropriately and avoid biased results.
