Confronta i metodi

	Disegno Sperimentale Fattoriale Adattivo Completo	Esperimento Fattoriale Completo
Campo	Disegno sperimentale	Disegno sperimentale
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1950s (factorial foundations); adaptive extensions prominent from 1990s onward	1926 (Fisher's foundational paper); codified by the 1950s–1960s
Ideatore	Rooted in Box & Hunter factorial design tradition; adaptive extensions formalised by Atkinson, Donev and others in optimal design theory	Ronald A. Fisher
Tipo	Experimental design	Experimental design
Fonte seminale	Atkinson, A., Donev, A., & Tobias, R. (2007). Optimum Experimental Designs, with SAS. Oxford University Press. ISBN: 978-0199296606	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
Alias	adaptive full-factorial design, sequential full factorial experiment, adaptive complete factorial design, dynamic full factorial trial	full factorial design, complete factorial design, 2^k factorial design, FFD
Correlati	5	6
Sintesi	An adaptive full factorial experiment is an experimental design that starts with a complete crossing of all factors and all their levels, then uses interim data to modify subsequent runs — dropping unpromising factor levels, adding new ones, or re-allocating replication — while preserving the full factorial structure within each phase. This integration of full factorial coverage with adaptive decision rules allows researchers to explore all main effects and interactions without committing to a fixed, inefficient run plan before any data are observed.	A full factorial experiment runs every possible combination of all chosen factor levels, making it the gold standard for simultaneously estimating main effects, two-way interactions, and higher-order interactions among multiple independent variables. Introduced through Ronald Fisher's foundational work on factorial designs in the 1920s and systematised by Box, Hunter, and Montgomery, it provides complete information about how factors act individually and in combination on an outcome.