Editing Nonparametric statistics (section)

==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 distribution]]s of the variables being assessed. The most frequently used tests include
* [[Analysis of similarities]]
* [[Anderson–Darling test]]: tests whether a sample is drawn from a given distribution
* [[Bootstrapping (statistics)|Statistical bootstrap methods]]: estimates the accuracy/sampling distribution of a statistic
* [[Cochran's Q test|Cochran's Q]]: tests whether ''k'' treatments in randomized block designs with 0/1 outcomes have identical effects
* [[Cohen's kappa]]: measures inter-rater agreement for categorical items
* [[Friedman test|Friedman two-way analysis of variance (Repeated Measures)]] by ranks: tests whether ''k'' treatments in randomized block designs have identical effects
* [[Empirical likelihood]]
* [[Kaplan–Meier estimator|Kaplan–Meier]]:  estimates the survival function from lifetime data, modeling censoring
* [[Kendall tau rank correlation coefficient|Kendall's tau]]: measures statistical dependence between two variables
* [[Kendall's W]]: a measure between 0 and 1 of inter-rater agreement.
* [[Kolmogorov–Smirnov test]]: tests whether a sample is drawn from a given distribution, or whether two samples are drawn from the same distribution.
* [[Kruskal–Wallis one-way analysis of variance]] by ranks: tests whether >&nbsp;2 independent samples are drawn from the same distribution.
* [[Kuiper's test]]: tests whether a sample is drawn from a given distribution, sensitive to cyclic variations such as day of the week.
* [[Logrank test]]: compares survival distributions of two right-skewed, censored samples.
* [[Mann–Whitney U]] or Wilcoxon rank sum test: tests whether two samples are drawn from the same distribution, as compared to a given alternative hypothesis.
* [[McNemar's test]]: tests whether, in 2 × 2 contingency tables with a dichotomous trait and matched pairs of subjects, row and column marginal frequencies are equal.
* [[Median test]]: tests whether two samples are drawn from distributions with equal medians.
* [[Pitman permutation test|Pitman's permutation test]]: a statistical significance test that yields exact ''p'' values by examining all possible rearrangements of labels.
* [[Rank product]]s: detects differentially expressed genes in replicated microarray experiments.
* [[Siegel–Tukey test]]: tests for differences in scale between two groups.
* [[Sign test]]: tests whether matched pair samples are drawn from distributions with equal medians.
* [[Spearman's rank correlation coefficient]]: measures statistical dependence between two variables using a monotonic function.
* [[Squared ranks test]]: tests equality of variances in two or more samples.
* [[Tukey–Duckworth test]]: tests equality of two distributions by using ranks.
* [[Wald–Wolfowitz runs test]]: tests whether the elements of a sequence are mutually independent/random.
* [[Wilcoxon signed-rank test]]: tests whether matched pair samples are drawn from populations with different mean ranks.
* Universal Linear Fit Identification: A Method Independent of Data, Outliers and Noise Distribution Model and Free of Missing or Removed Data Imputation.<ref>{{cite journal |last1=Adikaram |first1=K. K. L. B. |last2=Hussein |first2=M. A. |last3=Effenberger |first3=M. |last4=Becker |first4=T. |title=Universal Linear Fit Identification: A Method Independent of Data, Outliers and Noise Distribution Model and Free of Missing or Removed Data Imputation |journal=PLOS ONE |date=16 November 2015 |volume=10 |issue=11 |pages=e0141486 |doi=10.1371/journal.pone.0141486 |doi-access=free |pmid=26571035 |pmc=4646355 |bibcode=2015PLoSO..1041486A |language=en |issn=1932-6203}}</ref>