পদ্ধতির তুলনা করুন

	সিমুলেশন-সহায়ক অনুকল্প পরীক্ষণ গবেষণা	পাওয়ার অ্যানালাইসিস (Power Analysis)
ক্ষেত্র	গবেষণা নকশা	পরিসংখ্যান
পরিবার	Process / pipeline	Hypothesis test
উদ্ভবের বছর	1980s–1990s (bootstrap: 1979; permutation inference: mid-20th century; unified simulation-assisted framing: 1990s–2000s)	1969 (1st ed.); 1988 (seminal 2nd ed.)
প্রবর্তক	Bradley Efron (bootstrap framework); Phillip Good (permutation tests); Monte Carlo tradition traced to Stanislaw Ulam and John von Neumann	Jacob Cohen
ধরন	Quantitative research design integrating computational simulation with classical hypothesis testing	Sample size and power planning
মৌলিক উৎস	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
অপর নাম	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
সম্পর্কিত	3	5
সারসংক্ষেপ	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.