Bootstrapping is particularly useful in epidemiology for several reasons:
1. Non-parametric Nature: It does not assume a specific distribution of the data, making it flexible and robust, especially in dealing with non-normal or skewed distributions. 2. Small Sample Sizes: It provides reliable estimates even with small sample sizes, which is often a challenge in epidemiological studies. 3. Complex Statistics: It can be applied to complex statistics, such as medians, percentiles, and regression coefficients, which may not have straightforward analytical solutions.
