Comparer des méthodes

	Tests statistiques non paramétriques	Analyse de régression multiple
Domaine	Statistiques de recherche	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1947	1801
Auteur d'origine	Henry Mann and Donald Whitney	Carl Friedrich Gauss
Type	Method	Method
Source fondatrice	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.	Draper, N. R., & Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons.
Alias	rank-based tests, Mann-Whitney U, Kruskal-Wallis, distribution-free	MLR, multivariate regression, linear regression
Apparentées	3	4
Résumé	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.	Multiple regression analysis is a statistical method for modeling the relationship between a continuous dependent variable and two or more independent variables (predictors). Originating from Gauss's early 19th-century work and formalized by Draper and Smith (1966), it estimates linear equations predicting outcomes from multiple predictors while accounting for confounding relationships, making it indispensable in epidemiology, economics, psychology, and clinical research.