Data completeness refers to the extent to which all required data points are collected in a dataset. In epidemiology, incomplete data can lead to biased results, misinterpretation, and poor decision-making. For example, if a study on the prevalence of a disease lacks data from certain demographic groups, the findings may not be representative of the entire population.
