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| 시뮬레이션 지원 확증 연구× | 검정력 분석× | |
|---|---|---|
| 분야≠ | 연구설계 | 통계학 |
| 계열≠ | Process / pipeline | Hypothesis test |
| 기원 연도≠ | 1980s–2000s (widespread integration in behavioral and social sciences) | 1969 (1st ed.); 1988 (seminal 2nd ed.) |
| 창시자≠ | No single originator; tradition formalized through Monte Carlo methods (Metropolis & Ulam, 1949) applied to confirmatory designs | Jacob Cohen |
| 유형≠ | Quantitative hybrid design | Sample size and power planning |
| 원전≠ | Morey, R. D., Chambers, C. D., Aitken, M. R. F., Harris, C. R., Hoekstra, R., Lakens, D., Lewandowsky, S., Morey, C. C., Newman, D. P., Schonbrodt, F. D., Vanpaemel, W., Wagenmakers, E. J., & Zwaan, R. A. (2022). The Peer Reviewers' Openness Initiative: Incentivising open research practices through peer review. Royal Society Open Science, 3(1), 150547. link ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 |
| 별칭 | simulation-based confirmatory design, Monte Carlo confirmatory research, computational confirmatory study, simulation-enhanced hypothesis testing | sample size calculation, power calculation, sensitivity analysis, a priori power analysis |
| 관련 | 5 | 5 |
| 요약≠ | Simulation-assisted confirmatory research integrates computational simulation — most commonly Monte Carlo methods — into a hypothesis-driven, confirmatory study design. Before or alongside empirical data collection, the researcher runs simulated data under specified model assumptions to establish expected parameter distributions, verify statistical power, and anticipate the behavior of the chosen analysis. The empirical findings are then evaluated against those simulation-derived benchmarks, strengthening the evidential value of confirmatory conclusions. | 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. |
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