Parametric statistics

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Parametric statistics is a branch of statistics that is concerned with the analysis of and inference from data assuming that the underlying distribution, from which the observed data was drawn, can be described by a finite set of (unknown) parameters. In contrast, nonparametric statistics 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. Most well-known statistical methods are parametric. Regarding nonparametric (and semiparametric) models, Sir David Cox has said, "These typically involve fewer assumptions of structure and distributional form but usually contain strong assumptions about independencies".