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| 베이즈 가설 검정 연구× | 확증적 연구× | |
|---|---|---|
| 분야 | 연구설계 | 연구설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–2010 | 1934 (Popper); widely adopted in social sciences from 1960s onward |
| 창시자≠ | Harold Jeffreys (formal Bayes factor framework) | Karl Popper (falsificationism); formalized in behavioral sciences by Paul Meehl and others |
| 유형 | Quantitative research design | Quantitative research design |
| 원전≠ | Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press. ISBN: 978-0198503682 | Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson. ISBN: 978-0415278447 |
| 별칭 | Bayesian significance testing, Bayes factor hypothesis testing, BHT research, Bayesian inference testing | hypothesis-testing research, deductive research, theory-testing research, confirmatory study |
| 관련≠ | 5 | 4 |
| 요약≠ | Bayesian hypothesis testing research is a quantitative design in which competing hypotheses are evaluated by updating prior beliefs with observed data to produce posterior probabilities and Bayes factors. Unlike frequentist null-hypothesis significance testing, it quantifies the relative evidence for each hypothesis, supports optional stopping, and allows accumulation of evidence across studies without inflating Type I error rates. | Confirmatory research is a deductive quantitative design in which the researcher specifies hypotheses derived from existing theory before data collection, then tests whether the data support or refute those hypotheses. Unlike exploratory approaches that generate ideas from data, confirmatory research begins with an established theoretical framework, pre-registers predictions, and applies statistical tests to evaluate those predictions against empirical evidence. It is the backbone of hypothesis-driven social, behavioral, and health science inquiry. |
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