Shuffling is crucial for several reasons. It helps in maintaining the randomness in datasets, which is essential for the validity of epidemiological studies. Randomness ensures that the findings are not biased and that the conclusions drawn are reliable. Additionally, shuffling is used in permutation tests to assess the significance of observed associations between variables, and to avoid overfitting in predictive models.
