Developing a research project really does start with a question, and I hope that you are developing curiosity about the topic that you chose for your literature review. And remember, not all questions are equal in terms of what they can tell us. Some questions can be answered by looking within oneself, at one’s own experience and judgment. These questions are subjective. Other questions can be answered by looking out at the world, and making observations. These answers also are confirmable by other people, who might have observed the same answers in the same way. So, when we talk about the answers to these questions, other people can understand exactly what we mean.
Learn to use empirical Bayesian methods for estimating binomial proportions, through a series of examples drawn from baseball statistics. These methods are effective in estimating click-through rates on ads, success rates of experiments, and other examples common in modern data science. You'll learn both the theory and the practice behind empirical Bayesian methods, including computing credible intervals, performing Bayesian A/B testing, and fitting mixture models. Each example comes with R code that can be used to analyze your own data.