Bootstrapping is valuable in epidemiology for several reasons:
Confidence Intervals: It helps in constructing more accurate confidence intervals for epidemiological measures such as risk ratios, odds ratios, and incidence rates. Small Sample Sizes: It allows for robust statistical inference when dealing with small or limited datasets, which are common in epidemiological studies. Non-parametric Methods: Bootstrapping is a non-parametric method that does not assume a specific distribution for the data, making it versatile for various types of epidemiological data.
