= Nonparametric statistics
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Nonparametric statistics studies <statistical inference> without restricting the unknown <probability distribution> or target <function> to a fixed finite-dimensional <parametric statistical model>. In <density estimation> and <nonparametric regression>, the number of features that an <estimator> can represent may grow with the <sample size>. <Rank tests> offer another approach: the <probability distribution> of their <test statistic> under the <null hypothesis> can be obtained without specifying the common continuous <probability distribution>.
"Nonparametric" does not mean free of assumptions or tuning choices. A <kernel density estimator> still requires a <kernel for density estimation> and a <smoothing bandwidth>, and its interpretation depends on assumptions about the <statistical sample>. The freedom is useful when a prescribed shape, such as a single <normal distribution>, would hide features that the observations might reveal.
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