Σύγκριση μεθόδων

	Βαϋεσιανή Έρευνα Δειγματοληψίας	Επιβεβαιωτική Έρευνα
Πεδίο	Ερευνητικός Σχεδιασμός	Ερευνητικός Σχεδιασμός
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1980s–2000s (modern applied development)	1934 (Popper); widely adopted in social sciences from 1960s onward
Δημιουργός	Thomas Bayes (theorem, 1763); applied to survey methodology by Donald Rubin, Andrew Gelman, and others (1980s–2000s)	Karl Popper (falsificationism); formalized in behavioral sciences by Paul Meehl and others
Τύπος	Quantitative observational research design with Bayesian inference	Quantitative research design
Θεμελιώδης πηγή	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	Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson. ISBN: 978-0415278447
Εναλλακτικές ονομασίες	Bayesian survey analysis, Bayesian survey methodology, Bayesian polling, Bayesian questionnaire analysis	hypothesis-testing research, deductive research, theory-testing research, confirmatory study
Συναφείς	4	4
Σύνοψη	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.	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.