classical inference

Statistical inference is the process of drawing conclusions about a population parameter or distribution based on sample data. Classical methods, often referred to as frequentist statistics, involve making a tentative assumption (null hypothesis) about a parameter and using sample data to determine if this assumption can be rejected in favor of an alternative hypothesis.12

Bayesian methods offer an alternative by combining prior information or beliefs about a parameter with new sample data using Bayes' theorem. This results in a posterior probability distribution, which then guides the inference. While classical statistics views parameters as fixed constants, Bayesian methods allow for probability distributions to represent unknown parameters, a feature some find subjective, while proponents argue it makes subjectivity explicit. Both approaches aim to estimate population characteristics, such as means or proportions, using sample statistics.34