# Parametric statistics are more powerful statistics

## Non-parametric statistics are used with categorical and ordinal outcomes

**dichotomy of popular statistical terms**that are spoken commonly but not always understood by everyone.

__are used to assess differences and effects for__

**Parametric statistics**__. These statistical tests include__

**continuous outcomes****one-sample t-tests, independent samples t-tests, one-way ANOVA, repeated-measures ANOVA, ANCOVA, factorial ANOVA, multiple regression, MANOVA, and MANCOVA.**

__are used to assess differences and effects for:__

**Non-parametric statistics**1.

__-__

**Ordinal outcomes****One-sample median tests, Mann-Whitney U, Wilcoxon, Kruskal-Wallis, Friedman's ANOVA, Proportional odds regression**

2.

__-__

**Categorical outcomes****Chi-square, Chi-square Goodness-of-fit, odds ratio, relative risk, McNemar's, Cochran's Q, Kaplan-Meier, log-rank test, Cochran-Mantel-Haenszel, Cox regression, logistic regression, multinomial logistic regression**

3.

**Small sample sizes****(n < 30)**- Smaller sample sizes make it harder to meet the statistical assumptions associated with parametric statistics. Non-parametric statistics can generate valid statistical inferences in these situations.

4.

__for parametric tests -__

**Violations of statistical assumptions****Normality, Homogeneity of variance, Normality of difference scores**