Resamplausmenetelmä

Resamplausmenetelmä, often translated as resampling method, is a broad category of statistical techniques used to estimate properties of a population by repeatedly sampling from a given sample dataset. These methods are particularly useful when the underlying distribution of the population is unknown or when analytical solutions are complex to derive. The core idea is to treat the observed sample as a representation of the population and then generate many simulated samples from this observed sample. By analyzing these simulated samples, researchers can infer properties such as the sampling distribution of a statistic, confidence intervals, or the variability of an estimate.

A prominent example of a resampling method is the bootstrap. In bootstrapping, a new sample is created by randomly drawing observations from the original sample with replacement, until a new sample of the same size as the original is obtained. This process is repeated many times, creating a collection of bootstrap samples. Statistics calculated from each bootstrap sample can then be used to estimate the sampling distribution of the original statistic. Another common resampling technique is the jackknife, which involves systematically leaving out one observation at a time from the original sample and recalculating the statistic of interest. This helps to estimate the bias and variance of the statistic. Permutation tests are also a type of resampling method, used for hypothesis testing by randomly shuffling the labels of observations to create a null distribution. Resampling methods are computationally intensive but provide powerful tools for statistical inference, especially in situations where traditional parametric methods may not be applicable or assumptions are violated.