What are the major differences between Bayesian and Frequentist inference, and how do they impact the interpretation and results of statistical analyses?

Bayesian inference incorporates prior knowledge and updates beliefs with new data using Bayes’ theorem, providing a probabilistic interpretation. Frequentist inference relies on long-run frequency properties of estimators, focusing on data alone without prior beliefs. These approaches impact interpretation by influencing uncertainty quantification, hypothesis testing, and decision-making processes in analyses.

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