Comparer des méthodes

	Expérience factorielle fractionnaire en double aveugle	Méthodologie des surfaces de réponse (RSM)
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Hypothesis test
Année d'origine	1960s onward (combination widely used in pharmaceutical and food science research)	1951
Auteur d'origine	Fractional factorial: Box & Hunter (1961); double-blind convention: clinical trial methodology (mid-20th century)	George E. P. Box & K. B. Wilson
Type	Controlled experimental design with blinding and factor-space reduction	Second-order polynomial response surface model
Source fondatrice	Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Alias	double-blind FFE, blinded fractional factorial design, double-blind FFD, masked fractional factorial experiment	RSM, Central Composite Design, Box-Behnken Design, CCD
Apparentées	3	7
Résumé	A double-blind fractional factorial experiment combines two powerful methodological protections: fractional factorial design, which tests a carefully chosen subset of all possible factor combinations to achieve efficiency, and double-blind administration, which prevents both participants and assessors from knowing which treatment combination has been applied. The result is an experiment that is both resource-efficient and protected against expectation and assessment bias.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.