# BOOTSTRAPPING: RESAMPLING METHODOLOGY

**DOI:**https://doi.org/10.1007/1-4020-0611-X_84

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Researchers typically encounter many situations in which parametric statistical techniques are less than ideal. The *t*-statistic, for example, assumes that the data were sampled from a normal distribution. Of course, much real-world data follow distributions that are far from normal, and may in fact be quite skewed. Suppose a researcher is investigating data that is known to follow an exponential distribution. Clearly, it would take an extremely large sample and a great deal of manipulation (e.g., averages of averages), for the central limit theorem to apply. In many cases, there is no parametric test for the measurement of interest because we may not know the sampling distribution of that measurement and thus we would not have tractable analytic formulas for estimating such measures, for example, the difference between two medians (Mooney and Duval, 1993, p. 8).

There are a number of nonparametric statistical techniques that do not rely on distributional assumptions and often may be...

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