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| 模拟辅助假设检验研究× | 蒙特卡洛模拟× | |
|---|---|---|
| 领域≠ | 研究设计 | 决策 |
| 方法族≠ | Process / pipeline | MCDM |
| 起源年份≠ | 1980s–1990s (bootstrap: 1979; permutation inference: mid-20th century; unified simulation-assisted framing: 1990s–2000s) | 1949 |
| 提出者≠ | Bradley Efron (bootstrap framework); Phillip Good (permutation tests); Monte Carlo tradition traced to Stanislaw Ulam and John von Neumann | Metropolis, N., Ulam, S. |
| 类型≠ | Quantitative research design integrating computational simulation with classical hypothesis testing | Robustness wrapper — Monte Carlo uncertainty propagation |
| 开创性文献≠ | 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 DOI ↗ |
| 别名≠ | simulation-based hypothesis testing, Monte Carlo hypothesis testing, computational hypothesis testing, simulation-assisted inference | — |
| 相关≠ | 3 | 0 |
| 摘要≠ | 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. |
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