Statistics Notes: Bootstrap resampling methods

In medical research we study a sample of individuals to make inferences about a target population. Estimates of interest, such as a mean or a difference in proportions, are calculated, usually accompanied by a confidence interval derived from the standard error. The data from a single sample are used here to quantify the variation in the estimate of interest across (hypothetical) multiple samples from the same population.1 As we have only one sample we need to make assumptions about the data. Most methods of analysis are called parametric because they incorporate assumptions about the distribution of the data, such as that observations follow a normal distribution. Non-parametric methods avoid assumptions about distributions but generally provide only P values and not estimates of quantities of interest.2 For a given dataset the assumptions may not be met. In such cases there is an alternative way to estimate standard errors and confidence intervals without any reliance on assumed probability distributions. We use the sample dataset and apply a resampling procedure called …