Porovnat metody

	Pragmatický frakcionální faktorový experiment	Metodologie ploch odezvy (RSM)
Obor	Plánování experimentů	Plánování experimentů
Rodina	Process / pipeline	Hypothesis test
Rok vzniku	Fractional factorial designs: 1940s–1950s; pragmatic application: 2000s–2010s	1951
Tvůrce	Building on Fisher (1935); pragmatic adaptation by Collins, Murphy & Strecher (2007) via MOST framework	George E. P. Box & K. B. Wilson
Typ	Experimental design	Second-order polynomial response surface model
Původní zdroj	Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): New methods for more potent eHealth interventions. American Journal of Preventive Medicine, 32(5S), S112–S118.	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.
Další názvy	pragmatic FFE, fractional factorial trial, pragmatic factorial design, FFD in pragmatic settings	RSM, Central Composite Design, Box-Behnken Design, CCD
Příbuzné	4	7
Shrnutí	A pragmatic fractional factorial experiment applies fractional factorial design principles to real-world or clinical intervention research, enabling simultaneous evaluation of multiple intervention components in a resource-efficient fraction of the full factorial runs. Popularised through the Multiphase Optimization Strategy (MOST), it identifies which components of a multi-component intervention contribute meaningfully to outcomes before a confirmatory randomized trial is conducted.	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.