HSS 2381 Lecture Notes - Lecture 7: Relative Risk, Type I And Type Ii Errors, Effect Size
16 Nov 2016
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1- outcomes are not measured on an interval or ratio scale and/or. 2- assumptions for parametric test are severely violated: especially when sample sizes are small. Parametric: compute a statistic from a sample and relate it to a parameter in a reference population, assumes the sample comes from a known distribution, z-scores, t-tests, anova. Nonparametric: none of the above, ranked correlations, chi square, etc. Selection of a nonparametric test depends mostly on: number of groups being compared. Two versus three or more groups: type of comparison being made. Depended groups (within-subjects/repeated measures, correlated groups designs) The term non-parametric refers to the fact that the chi-square tests do not require assumptions about population parameters nor do they test hypotheses about population parameters. T tests and analysis of variance are parametric tests and they do include assumptions about parameters and test hypotheses about parameters.
