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Parametric statistics
From Wikipedia, the free encyclopedia
Parametric statistics are statistics that estimate population parameters.
Parametric inferential statistical methods are mathematical procedures for statistical hypothesis testing which assume that the distributions of the variables being assessed belong to known parametrized families of probability distributions. In that case we speak of parametric model.
All parametric tests involve (a) estimating at least one population parameter, (b) assumptions about the distribution of the population from which the data were randomly sampled, and (c) assumptions about the measurement of the dependent variable.
For example, analysis of variance (ANOVA) assumes that the underlying distributions are normally distributed and that the variances of the distributions being compared are similar. The Pearson product-moment correlation coefficient also assumes normality.
While parametric techniques are robust – that is, they often retain considerable power to detect differences or similarities even when these assumptions are violated – some distributions violate the assumptions so markedly that a non-parametric alternative is more likely to detect a difference or similarity.
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