A recent defense of the Bayesian approach: https://www.astralcodexten.com/p/in-continued-defense-of-non-frequentist https://www.astralcodexten.com/p/in-continue

A recent defense of the Bayesian approach: https://www.astralcodexten.com/p/in-continued-defense-of-non-frequentist https://www.astralcodexten.com/p/in-continued-defense-of-non... (and Kling's response: https://arnoldkling.substack.com/p/what-is-probability https://arnoldkling.substack.com/p/what-is-probability) Likelihood statistics is another approach that combines the classical and Bayesian approaches. Its main benefit is that it does not rely on prior probabilities about an event. Instead, you can use MLE (maximum likelihood estimation, https://en.wikipedia.org/wiki/Maximum_likelihood_estimation https://en.wikipedia.org/wiki/Maximum_likelihood_estimation) to estimate your parameter, with certain guarantees based on which estimator you choose for the parameter and how you calculate it. The downside of this approach is that it requires a strong assumption: that you know the distribution of outcomes in advance. Side note: the only time you'll know the population parameter is if you set it yourself (i.e., in a simulation). edit: Title should also probably be changed to the article's title ("How I Teach Statistics")