Salīdzināt metodes

	Danna daudzkārtējais salīdzināšanas tests	Frīdmana tests	Nemenyi pārbaude pēc Frīdmens testēšanas
Nozare	Statistika	Statistika	Statistika
Saime	Hypothesis test	Hypothesis test	Hypothesis test
Izcelsmes gads	1964	1937	1963
Autors	Olive Jean Dunn	Milton Friedman	Peter Nemenyi
Tips	Nonparametric pairwise comparison	Nonparametric repeated-measures comparison (by ranks)	Nonparametric post-hoc multiple comparison
Pirmavots	Dunn, O.J. (1964). Multiple Comparisons Using Rank Sums. Technometrics, 6(3), 241–252.	Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200), 675–701.	Nemenyi, P. (1963). Distribution-Free Multiple Comparisons. PhD thesis, Princeton University.
Citi nosaukumi	Dunn's post-hoc test, Kruskal-Wallis post-hoc, Dunn Testi — Kruskal-Wallis Post-Hoc	Friedman two-way analysis of variance by ranks, Friedman rank test, Friedman Testi	Nemenyi Testi — Friedman Post-Hoc, Nemenyi multiple comparison test, Nemenyi procedure
Saistītās	5	2	5
Kopsavilkums	Dunn's test is a nonparametric post-hoc procedure introduced by Olive Jean Dunn in 1964 to identify which specific pairs of groups differ significantly after a Kruskal-Wallis test has returned a significant overall result. It compares groups pairwise using rank sums and applies a multiple-comparison correction — most commonly Bonferroni or Holm — to control the family-wise error rate.	The Friedman test is a nonparametric hypothesis test that compares three or more related conditions measured on the same blocks or subjects, serving as the rank-based alternative to repeated-measures ANOVA. It was introduced by Milton Friedman in 1937 and works on ordinal or continuous data without assuming normality.	The Nemenyi test is a nonparametric post-hoc multiple comparison procedure introduced by Peter Nemenyi in his 1963 Princeton doctoral thesis. It is applied after a significant Friedman test to identify which specific pairs of conditions differ from each other in a repeated-measures or blocked design.