Σύγκριση μεθόδων

	Μπεϋζιανή Παρατηρησιακή Ποσοτική Έρευνα	Μπεϋζιανή Συμπερασματολογία
Πεδίο	Ερευνητικός Σχεδιασμός	Στατιστική
Οικογένεια	Process / pipeline	Bayesian methods
Έτος προέλευσης	1990s–2000s (systematic application to observational research)	1763
Δημιουργός	Thomas Bayes (foundational theorem, 1763); modern applied form developed by Sander Greenland, Andrew Gelman, and colleagues (1990s–2000s)	Thomas Bayes; Pierre-Simon Laplace
Τύπος	Quantitative non-experimental research design with Bayesian inference	Probabilistic inference paradigm
Θεμελιώδης πηγή	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.
Εναλλακτικές ονομασίες	Bayesian observational study, Bayesian non-experimental quantitative design, Bayesian causal observational analysis, BOQR	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Συναφείς	4	3
Σύνοψη	Bayesian observational quantitative research applies Bayesian statistical inference to data collected without experimental manipulation — surveys, administrative records, registries, or secondary datasets. Instead of relying solely on p-values and confidence intervals, the analyst encodes prior knowledge about parameters as probability distributions, updates them with observed data via Bayes' theorem, and reports conclusions as posterior probability statements. The approach is especially valued in epidemiology, social science, and health services research where randomisation is impossible or unethical.	Bayesian inference is a statistical paradigm in which probability represents degrees of belief rather than long-run frequencies. It encodes prior knowledge about parameters in a prior distribution, combines that prior with the likelihood of observed data via Bayes' theorem, and produces a posterior distribution that quantifies updated uncertainty. The foundational theorem was published posthumously by Thomas Bayes in 1763 and subsequently systematized by Pierre-Simon Laplace in his 1812 Théorie analytique des probabilités.