Introduction
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 the sampling distribution of that measurement may be unknown and thus there would be no 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...
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Friedman, L.W., Friedman, H.H. (2013). Bootstrapping: Resampling Methodology. In: Gass, S.I., Fu, M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1153-7_84
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