Comparar métodos

	Análise de Sensibilidade Global	Desenho de Experimentos
Área	Simulação	Delineamento experimental
Família	Process / pipeline	Process / pipeline
Ano de origem	1973–2001	1935
Autor original	I.M. Sobol (indices, 2001); Morris (screening, 1991); Cukier et al. (FAST, 1973)	Ronald A. Fisher
Tipo	Variance-based sensitivity decomposition	Experimental planning framework
Fonte seminal	Sobol, I.M. (2001). Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates. Mathematics and Computers in Simulation, 55(1–3), 271–280.	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Outros nomes	variance decomposition, Sobol indices, Morris screening, FAST method	DOE, experimental design, factorial experimentation, planned experimentation
Relacionados	4	3
Resumo	Global sensitivity analysis (GSA) is a family of techniques that decompose the variance of a model's output across its input parameters, quantifying how much each input — and each combination of inputs — contributes to the total uncertainty in the result. Sobol's variance-based indices (2001), Morris's one-at-a-time (OAT) screening (1991), and the Fourier Amplitude Sensitivity Test (FAST, first proposed by Cukier et al. in 1973) are the three most widely used approaches. Together they serve as the standard toolkit for identifying which parameters drive model behaviour and which can be safely fixed.	Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.