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Use this tag to ask about the nature of nonparametric or parametric methods, or the difference between the two. Nonparametric methods generally rely on few assumptions about the underlying distributions, whereas parametric methods make assumptions that allow data to be described by a small number of parameters.

Most statistical procedures derive their justification from a probability model of the observations to which they are applied. Such a model posits that the data appear to be related in a specific way to draws from some probability distribution that is an unknown member of some family of distributions. The family of distributions for a parametric procedure can be described in a natural way by a finite set of real numbers, the "parameters." Examples include the family of Binomial distributions (which can be parameterized by the chance of a "success") and the family of Normal distributions (usually parameterized by an expectation $\mu$ and variance $\sigma^2$). When such a description is not possible, the procedure is termed "nonparametric." Wikipedia provides a list of some non-parametric procedures.