方法证据记录
Simulation-assisted hypothesis testing research
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.
源记录
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Simulation-Assisted Hypothesis Testing Research
分类方法记录 · process-pipeline / research-design
- Efron, B., & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC. · ISBN 978-0412042317
- Good, P. I. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. · ISBN 978-0387988641
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