Comparer des méthodes

	Expérience factorielle complète en double aveugle	Essai Contrôlé Randomisé Factoriel
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1935 (factorial foundations, Fisher); double-blind combined application from 1950s onward	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
Auteur d'origine	Full factorial design: Ronald A. Fisher; double-blind masking: formalized in clinical research mid-20th century	R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)
Type	Controlled experimental design with masking	Experimental trial design
Source fondatrice	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443	Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47(4), 498–504.
Alias	double-masked full factorial design, double-blind complete factorial experiment, blinded full factorial RCT, double-blind factorial trial	Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization
Apparentées	4	6
Résumé	A double-blind full factorial experiment crosses every level of every independent variable to create all possible treatment combinations, while ensuring that neither participants nor outcome assessors know which condition each participant has been assigned to. This design simultaneously achieves comprehensive examination of main effects and all interactions, and protection against performance and detection bias through blinding — making it especially valuable in clinical, pharmacological, and behavioral research.	A factorial randomized controlled trial (factorial RCT) is an experimental design in which participants are randomly assigned to every possible combination of two or more independent factors (treatments or intervention components) simultaneously. This allows researchers to estimate the main effect of each factor and their interactions within a single, efficient trial, rather than running separate experiments for each factor.