# Non-parametric statistics are robust to small sample sizes

## The right way to conduct statistics

**There are lies, damn lies, and statistics.**" Statistics can be

**misleading**from both the standpoint of the person conducting the statistics and the person that is interpreting the analyses. Many between-subjects studies have

**small sample sizes (n < 20)**and

**statistical assumptions for parametric statistics cannot be met**.

For basic researchers that operate day in and day out with small sample sizes, the answer is to

**use non-parametric statistics**. Non-parametric statistical tests such as the

**. These tests can yield**

__Mann-Whitney U__,__Kruskal-Wallis__,__Wilcoxon__, and__Friedman's ANOVA__are robust to violations of statistical assumptions and skewed distributions**interpretable medians, interquartile ranges, and**.

*p*-valuesNon-parametric statistics are also useful in the

**social sciences**due to the inherent

**measurement error associated with assessing human behaviors, thoughts, feelings, intelligence, and emotional states**. The

**underlying algebra**associated with

__relies on__

**psychometrics****intercorrelations**amongst constructs or items. Correlations can easily be

**skewed by outlying observations and measurement error**. Therefore, in concordance with

**mathematical and empirical reasoning**, non-parametric statistics should be used often for between-subjects comparisons of

**.**

__surveys__, instruments, and psychological measures