Bayesian Inference vs. Frequentist Inference

Bayesian Inference and Frequentist Inference represent two fundamental approaches to statistical analysis, each with its unique philosophy and methodology. Bayesian inference, rooted in Bayes’ theorem, updates the probability of a hypothesis as more evidence or data becomes available, viewing probability as a measure of belief. Frequentist inference, in contrast, interprets probability as the frequency of an event in repeated experiments, focusing on long-run behavior and relying on fixed parameters. Understanding these approaches’ differences in interpreting probability, incorporating prior information, and applying their methods to data is important for effective statistical analysis.