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	Disegno Bayesiano Ex Post Facto	Inferenza Bayesiana
Campo	Disegno della ricerca	Statistica
Famiglia	Process / pipeline	Bayesian methods
Anno di origine	1964 (Kerlinger ex post facto); Bayesian integration from 1990s–2000s onward	1763
Ideatore	Frederick N. Kerlinger (ex post facto framework); Bayesian extension draws on Laplace and modern Bayesian statistics	Thomas Bayes; Pierre-Simon Laplace
Tipo	Quantitative observational research design with Bayesian inference	Probabilistic inference paradigm
Fonte seminale	Kerlinger, F. N. (1973). Foundations of Behavioral Research (2nd ed.). Holt, Rinehart and Winston.	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.
Alias	Bayesian causal-comparative design, Bayesian after-the-fact design, Bayesian observational causal design, Bayesian retrospective causal study	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Correlati	5	3
Sintesi	Bayesian ex post facto design investigates possible causal relationships among variables that have already occurred, without researcher manipulation of those variables, and quantifies uncertainty about those relationships using Bayesian statistical inference. The researcher selects groups that differ on an outcome or a presumed cause after the fact, then uses prior knowledge and observed data together — via Bayes' theorem — to estimate credible effect sizes, group differences, or predictors.	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.