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  "body": "\n## Summary\n\nNonparametric statistics is the part of statistics that does not assume the law of the data belongs to a family described by finitely many parameters.\nIt uses models of infinite dimension, such as all continuous distributions on $\\R$ or all smooth densities.\nIt also includes distribution-free methods, whose behaviour under the null hypothesis does not depend on the unknown law.\nTypical tools are the empirical distribution function, rank tests, [[kernel density estimation|ZV65XMJ9A4]] and the bootstrap.\n\n## Explanations\n\n### L1 human\n\nA parametric method assumes, for example, that the data are normal and then only estimates a mean and a variance.\nA nonparametric method lets the data speak for the shape of the law.\nFor instance, a histogram or a kernel density estimate shows the shape of the data without assuming a bell curve.\nThe price is that more data are needed for the same precision.\n",
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