An interactive flowchart / decision tree to help you decide which statistical test to use, with descriptions of each test and links to carry them out in R, SPSS and STATA. Made by Matthew Jackson. Based on a text book by Andy Field.. Choosing the Correct Statistical Test in SAS, Stata, SPSS and R. The following table shows general guidelines for choosing a statistical analysis. We emphasize that these are general guidelines and should not be construed as hard and fast rules. Usually your data could be analyzed in multiple ways, each of which could yield legitimate answers.

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When comparing more than two sets of numerical data, a multiple group comparison test such as one-way analysis of variance (ANOVA) or Kruskal-Wallis test should be used first. If they return a statistically significant p value (usually meaning p < 0.05) then only they should be followed by a post hoc test to determine between exactly which two.. Assumptions: testing the assumptions required for a statistical analysis. Equality of variance: Data are normally distributed - Levene's test, Bartlett test (also Mauchly test for sphericity in repeated measures analysis). Data are non-parametric - Ansari-Bradley, Mood test, Fligner-Killeen test.