In this post, I summarize a series of resources to get started with Bayesian Statistics. I compiled these references based on my experience and opinion as to what a good introduction and next steps are in this process. This is not an academic curriculum or anything tremendously rigorous, but it is a comprehensive list that will surely get you embarked on the journey to revisiting/starting your statistics. Many of the references below were recommended to me in several workshops I've attended, and I want to share with those like me that want to be better at statistics and Machine Learning (ML). The first resource I can think of out there for beginners interested in Bayesian statistics and modeling is Richard McElreath's Statistical Rethinking.

What if you could make software testing simple? What if it could be done without all the conversations, questions, defect reports, and metrics? We've been promised artificial intelligence (AI) as the solution to all problems related to testing, especially by those who have never tested--those who believe that what we do as testers is little more than tapping screens to make comparisons. Although I've stated that AI is coming and will change software testing forever (eventually), we're not there yet--not even close. But that doesn't mean we can't use AI to support our testing efforts.