Random over sampling is critical in epidemiological studies for several reasons:
Improved Model Performance: It helps in improving the performance of predictive models by providing balanced datasets, which are essential for training algorithms effectively. Enhanced Statistical Power: By balancing the dataset, researchers can achieve better statistical power, making it easier to detect significant associations between variables. Better Representation: It ensures that the minority class is adequately represented, which is crucial for studying rare diseases or conditions.
