Several resampling methods are commonly used in epidemiology, including:
Bootstrap: This method involves repeatedly sampling with replacement from the observed data and calculating the statistic of interest for each sample. It helps estimate the sampling distribution of a statistic. Jackknife: Similar to the bootstrap, but involves systematically leaving out one observation at a time from the sample set and calculating the statistic of interest. It is useful for estimating the bias and variance of a statistic. Permutation Tests: Involves rearranging the observed data points to test a hypothesis, often used to test for differences between groups. Cross-Validation: Primarily used in predictive modeling, it involves partitioning the data into subsets, training the model on some subsets, and validating it on the remaining data.
