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| Ricerca di Test d'Ipotesi Bayesiano× | Ricerca Bayesiana su Dati di Sondaggio× | |
|---|---|---|
| Campo | Disegno della ricerca | Disegno della ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–2010 | 1980s–2000s (modern applied development) |
| Ideatore≠ | Harold Jeffreys (formal Bayes factor framework) | Thomas Bayes (theorem, 1763); applied to survey methodology by Donald Rubin, Andrew Gelman, and others (1980s–2000s) |
| Tipo≠ | Quantitative research design | Quantitative observational research design with Bayesian inference |
| Fonte seminale≠ | Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press. ISBN: 978-0198503682 | Gelman, A., & Carlin, J. B. (2007). Some issues on the foundations of statistics. In A. Gelman & J. B. Carlin (Eds.), Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891 |
| Alias | Bayesian significance testing, Bayes factor hypothesis testing, BHT research, Bayesian inference testing | Bayesian survey analysis, Bayesian survey methodology, Bayesian polling, Bayesian questionnaire analysis |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | 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. | Bayesian survey research applies Bayesian statistical inference to survey data, combining prior knowledge or beliefs about population parameters with observed questionnaire responses to produce posterior probability distributions. Unlike null-hypothesis significance testing, this approach quantifies uncertainty directly, incorporates prior evidence, and yields probabilistic statements about parameters of interest — making it especially powerful for small samples, sequential data collection, and contexts where substantive prior knowledge exists. |
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