Missing data in epidemiological studies can arise from various sources, including participant drop-out, non-response to specific questions, or data entry errors. The importance of data imputation lies in its ability to:
1. Reduce Bias: Missing data can lead to biased results if the missingness is related to the outcome of interest. 2. Increase Statistical Power: By filling in the gaps, data imputation maximizes the use of available data, leading to more robust and statistically significant findings. 3. Improve Generalizability: Properly imputed data can enhance the representativeness of the study population, making it easier to generalize the findings to a broader population.
