Parametric statistics

Parametric statistics are used when the outcome is continuous and statistical assumptions are met

Parametric statistics are used when the outcome is continuous and the statistical assumptions of normality and homogeneity of variance are met. Parametric statistics provide more precise and accurate inferences. There is also more statistical power associated with the use of parametric statistics. This increase in power comes directly from the precision and accuracy found in continuous level measurement.

Types of parametric statistics

Click on a button below to access the methods for conducting and interpreting parametric statistics.
One-Sample t-test
Independent Samples t-test
Repeated-Measures t-test
ANOVA
Repeated-Measures ANOVA
Greenhouse-Geisser
Pearson's r
ANCOVA
Fixed-Effects ANOVA
Random-Effects ANOVA
Mixed-Effects ANOVA
Multiple Regression
MANOVA
MANCOVA
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There are several types of parametric statistics: One-sample t-test, independent samples t-test, repeated-measures t-test, one-way ANOVA, repeated-measures ANOVA, Greenhouse-Geisser, Pearson's r, ANCOVA, fixed-effects ANOVA, random-effects ANOVA, mixed-effects ANOVA, multiple regression, MANOVA, and MANCOVA.