Bootstrapsimulaties

Bootstrapsimulaties, also known as bootstrap simulations or simply bootstrapping, is a statistical method used to estimate the sampling distribution of a statistic. It is a resampling technique that treats the observed sample as a population from which new samples, called bootstrap samples, are drawn with replacement. This process is repeated many times to generate a collection of bootstrap statistics, which collectively approximate the sampling distribution of the original statistic.

The core idea behind bootstrapping is that the original sample provides a reasonable approximation of the underlying population. By repeatedly drawing samples from this observed sample with replacement, we can simulate the process of drawing multiple samples from the true population. Each bootstrap sample will differ slightly from the original sample and from other bootstrap samples, reflecting the variability that would be expected if we were to draw multiple samples from the actual population.

This method is particularly useful when the theoretical sampling distribution of a statistic is unknown or difficult to derive analytically. It can be applied to a wide range of statistics, including means, medians, variances, regression coefficients, and more. The primary applications of bootstrapping include estimating the standard error of a statistic, constructing confidence intervals, and performing hypothesis tests. It is a versatile and powerful tool in statistical inference, especially in situations where traditional parametric methods may not be applicable or feasible.