विधियों की तुलना करें

	गैर-पैरामीट्रिक सांख्यिकीय परीक्षण	प्रसरण विश्लेषण (ANOVA)
क्षेत्र	अनुसंधान सांख्यिकी	अनुसंधान सांख्यिकी
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1947	1925
प्रवर्तक	Henry Mann and Donald Whitney	Ronald A. Fisher
प्रकार	Method	Method
मौलिक स्रोत	Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50–60.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.
उपनाम	rank-based tests, Mann-Whitney U, Kruskal-Wallis, distribution-free	ANOVA, F-test
संबंधित	3	4
सारांश	Nonparametric (distribution-free) tests are statistical methods for hypothesis testing that do not assume data follow a specific probability distribution (e.g., normal), making them robust to departures from normality, outliers, and ordinal data. The Mann-Whitney U test (1947) and Kruskal-Wallis test (1952) extend hypothesis testing beyond the constraints of parametric assumptions. Essential in biology, medicine, psychology, and any field where data are non-normal, highly skewed, or measured on ordinal scales (rankings, ratings), nonparametric tests provide valid inference when parametric assumptions fail.	ANOVA is a parametric statistical method developed by Ronald A. Fisher in 1925 that tests whether means differ significantly across three or more independent groups. By partitioning total variance into between-group and within-group components, ANOVA determines whether observed differences are likely due to treatment effects or random variation, making it fundamental to comparative research across medicine, psychology, agriculture, and engineering.