Interpret this flowcharts in narrative.

Choosing a nonparametric test Non-parametric tests don't make as many assumptions about the data, and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren't as strong as with parametric tests. Predictor variable Outcome variable Use in place of... Spearman's r . Quantitative . Quantitative Pearson's r Chi square test of . Categorical . Categorical Pearson's / independence Sign test . Categorical . Quantitative One-sample t- test Kruskal-Wallis H . Categorical . Quantitative ANOVA . 3 or more groups ANOSIM Categorical Quantitative MANOVA . 3 or more . 2 or more outcome groups variables Wilcoxon Rank-Sum Categorical Quantitative Independent t- test . 2 groups . groups come from test different populations Wilcoxon Signed-rank Categorical Quantitative Paired t-test test . 2 groups . groups come from the same populationChoosing a statistical test This flowchart helps you choose among parametric tests Categorical Predictor variable: Quantitative categorical or quantitative? Outcome variable: Outcome variable: categorical or quantitative? categorical or quantitative? Categorical Quantitative Categorical Quantitative Choose a Do a comparison Logintic How many predictor mon-parametric test all means tout regression One More than one How many groups are being compared? Simple regression Multiple regression Ture More than two T-test How many outcome variables? More than one ANOVA MANOVA