เปรียบเทียบวิธี

	Simulation-assisted hypothesis testing research	การวิเคราะห์อำนาจการทดสอบ
สาขาวิชา	การออกแบบการวิจัย	สถิติศาสตร์
ตระกูล	Process / pipeline	Hypothesis test
ปีกำเนิด	1980s–1990s (bootstrap: 1979; permutation inference: mid-20th century; unified simulation-assisted framing: 1990s–2000s)	1969 (1st ed.); 1988 (seminal 2nd ed.)
ผู้ริเริ่ม	Bradley Efron (bootstrap framework); Phillip Good (permutation tests); Monte Carlo tradition traced to Stanislaw Ulam and John von Neumann	Jacob Cohen
ประเภท	Quantitative research design integrating computational simulation with classical hypothesis testing	Sample size and power planning
แหล่งต้นตำรับ	Efron, B., & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC. ISBN: 978-0412042317	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
ชื่อเรียกอื่น	simulation-based hypothesis testing, Monte Carlo hypothesis testing, computational hypothesis testing, simulation-assisted inference	sample size calculation, power calculation, sensitivity analysis, a priori power analysis
ที่เกี่ยวข้อง	3	5
สรุป	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.	Power analysis is a planning and evaluation technique that quantifies the probability of detecting a real effect of a given magnitude at a chosen significance level. It links four quantities — sample size, effect size, significance level (alpha), and statistical power (1 minus beta) — so that researchers can determine the sample size needed before data collection or evaluate the sensitivity of a completed study.