Σύγκριση μεθόδων

	Πειραματικός Σχεδιασμός Προσαρμοστικού Πλήρους Παραγοντικού Πειράματος	Πειραματικός Σχεδιασμός Κλασματικού Παραγοντικού Πειράματος
Πεδίο	Πειραματικός Σχεδιασμός	Πειραματικός Σχεδιασμός
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1950s (factorial foundations); adaptive extensions prominent from 1990s onward	1945 (Finney); broader development 1950s–1970s by Box, Hunter
Δημιουργός	Rooted in Box & Hunter factorial design tradition; adaptive extensions formalised by Atkinson, Donev and others in optimal design theory	D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work
Τύπος	Experimental design	Quantitative experimental design
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	adaptive full-factorial design, sequential full factorial experiment, adaptive complete factorial design, dynamic full factorial trial	fractional factorial design, FFD, 2^(k-p) design, fractional replication
Συναφείς	5	4
Σύνοψη	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 fractional factorial experiment is a resource-efficient experimental design that tests only a carefully chosen fraction of all possible factor-level combinations. By exploiting the principle that high-order interactions are usually negligible, it identifies the main effects and low-order interactions of k factors using far fewer runs than a full factorial design — making it the workhorse of industrial and engineering screening experiments.