Comparar métodos

	Pesquisa de Teste de Hipóteses Assistida por Simulação	Simulação de Monte Carlo
Área	Delineamento de pesquisa	Tomada de decisão
Família	Process / pipeline	MCDM
Ano de origem	1980s–1990s (bootstrap: 1979; permutation inference: mid-20th century; unified simulation-assisted framing: 1990s–2000s)	1949
Autor original	Bradley Efron (bootstrap framework); Phillip Good (permutation tests); Monte Carlo tradition traced to Stanislaw Ulam and John von Neumann	Metropolis, N., Ulam, S.
Tipo	Quantitative research design integrating computational simulation with classical hypothesis testing	Robustness wrapper — Monte Carlo uncertainty propagation
Fonte seminal	Efron, B., & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC. ISBN: 978-0412042317	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Outros nomes	simulation-based hypothesis testing, Monte Carlo hypothesis testing, computational hypothesis testing, simulation-assisted inference	—
Relacionados	3	0
Resumo	Simulation-assisted hypothesis testing research replaces or supplements analytical probability theory with computational simulation — resampling, permutation, or Monte Carlo methods — to construct null distributions and evaluate hypotheses. Rather than assuming a parametric distribution and consulting a table, the researcher generates thousands of simulated datasets from the observed data or a specified model, building an empirical null distribution against which the observed test statistic is compared. The approach is especially valuable when analytic assumptions (normality, large samples) cannot be met.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.