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 Bayesā€™ 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.