Methods
Non-parametric (or distribution-free) inferential statistical methods are mathematical procedures for statistical hypothesis testing which, unlike parametric statistics, make no assumptions about the probability distributions of the variables being assessed. The most frequently used tests include
- Anderson–Darling test
- Statistical Bootstrap Methods
- Cochran's Q
- Cohen's kappa
- Friedman two-way analysis of variance by ranks
- Kaplan–Meier
- Kendall's tau
- Kendall's W
- Kolmogorov–Smirnov test
- Kruskal-Wallis one-way analysis of variance by ranks
- Kuiper's test
- Logrank Test
- Mann–Whitney U or Wilcoxon rank sum test
- McNemar's test
- median test
- Pitman's permutation test
- Rank products
- Siegel–Tukey test
- Spearman's rank correlation coefficient
- Wald–Wolfowitz runs test
- Wilcoxon signed-rank test.
Read more about this topic: Non-parametric Statistics
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