مقایسهٔ روش‌ها

	آزمون تعقیبی کانوور-ایمن	آزمون مقایسات چندگانه دان	آزمون فریدمن
حوزه	آمار	آمار	آمار
خانواده	Regression model	Hypothesis test	Hypothesis test
سال پیدایش	1979	1964	1937
پدیدآور	Conover & Iman	Olive Jean Dunn	Milton Friedman
نوع	Nonparametric post-hoc multiple comparison	Nonparametric pairwise comparison	Nonparametric repeated-measures comparison (by ranks)
منبع بنیادین	Conover, W. J. & Iman, R. L. (1979). On Multiple-Comparisons Procedures. Technical Report LA-7677-MS, Los Alamos Scientific Laboratory.	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.
نام‌های دیگر	Conover-Iman post-hoc test, Conover post-hoc test, Conover-Iman Post-Hoc Testi	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
مرتبط	3	5	2
خلاصه	The Conover-Iman test is a rank-based post-hoc procedure, introduced by Conover and Iman in 1979, that identifies which pairs of groups differ after a significant Kruskal-Wallis or Friedman test. It builds a t-style statistic on the pooled ranks and is generally more powerful than the comparable Dunn test.	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.