# parametric statistics

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## parametric statistics

inferential statistics that assume that the population from which the SAMPLE has been drawn has a particular form, i.e. they involve hypotheses about population parameters. These assumptions are generally that the populations involved have a NORMAL DISTRIBUTION, that they have equal variances (see MEASURES OF DISPERSION) and that the data are at interval level (see CRITERIA AND LEVELS OF MEASUREMENT). Examples are the PEARSON PRODUCT MOMENT CORRELATION COEFFICIENT, multiple regression, and analysis of variance. Such procedures use all available information and tests are more powerful than nonparametric tests. In sociology, the problem of data that are not normally distributed in the population frequently arises. A transformation of scale, a reliance on the robustness of the technique, or a move to a nonparametric equivalent are the available solutions. Compare NONPARAMETRIC STATISTICS.
References in periodicals archive ?
All data were subjected to homogeneity (Hartley) and normality (ShapiroWilk) tests to verify the assumptions of parametric statistics. Data were subjected to analysis of variance (ANOVA) and the Tukey test at 5% probability, using the software SISVAR 5.4 (Ferreira 2011).
The real issue here is that if his indictment of parametric statistics is justified, then we need to pay attention, because psychologists and other social scientists use these tools daily.
There is a brief chapter on descriptive statistics, e.g., mean and range, then a section on parametric statistics (t-test, regression), a section on non-parametric statistics and one that we seldom encounter where multivariate statistics are covered.
FDA approval studies have parametric statistics based on the bell-shaped curve.
However, the entire history of the development of parametric statistics and much of the attention directed to research design has been devoted to establishing causality (see Tabachnick & Fidell, 1989).
Additional analyses of travel factors were completed by using Kruskal-Wallis one-way ANOVA by ranks tests because the data were too skewed to use parametric statistics (see Table 4).

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