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Parametric statistics
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<p> Parametric statistics is a branch of statistics which leverages models based on a fixed (finite) set of <a href="/facts/Statistical_parameter/fkJju25M">parameters</a>. Conversely <a href="/facts/Nonparametric_statistics/svTDJHTb">nonparametric statistics</a> does not assume explicit (finite-parametric) mathematical forms for distributions when modeling data. However, it may make some assumptions about that distribution, such as continuity or symmetry, or even an explicit mathematical shape but have a model for a distributional parameter that is not itself finite-parametric. </p><p>Most well-known statistical methods are parametric. Regarding nonparametric (and semiparametric) models, <a href="/facts/David_Cox_(statistician)/RM7agpV5">Sir David Cox</a> has said, "These typically involve fewer assumptions of structure and distributional form but usually contain strong assumptions about independencies". </p>