I remember the frequentist approach taught in introductory stats classes never making sense to me. I didn't want to shove "stats" into the back of my brain and

I remember the frequentist approach taught in introductory stats classes never making sense to me. I didn't want to shove "stats" into the back of my brain and just focus on graduating. I genuinely wanted to understand the world a little better. I began to research alternative approaches to modeling and conducting inference a few years ago. Discovering Bayesian Inference has had a large impact on the way I think and conduct research. There's a lot of hype and uncertainty about what "Bayesian" actually means. Here's a compact definition that I hope will attract some interest: Bayesian Inference allows you to explicitly quantify your prior beliefs and get a more complete picture of uncertainty when modeling something. If you'd like to learn more, the links below should be helpful. Introduction to Bayes' Theorem (short): https://www.countbayesie.com/blog/2015/2/18/bayes-theorem-with-lego https://www.countbayesie.com/blog/2015/2/18/bayes-theorem-wi... Bayesian A/B testing example (short): https://www.countbayesie.com/blog/2015/4/25/bayesian-ab-testing https://www.countbayesie.com/blog/2015/4/25/bayesian-ab-test... If you're interested in spending some time learning about applied Bayesian Inference, I highly recommend Statistical Rethinking. The book doesn't assume a strong mathematical background and its filled with practical examples. https://xcelab.net/rm/statistical-rethinking/ https://xcelab.net/rm/statistical-rethinking/ McElreath is currently working on a second edition of that textbook, due around 2020: http://elevanth.org/blog/2018/07/14/statistical-rethinking-edition-2-eta-2020/ http://elevanth.org/blog/2018/07/14/statistical-rethinking-e...