Bayesian statistics is often presented as a powerful alternative to traditional statistical methods, but for many analysts trained in a frequentist framework, it can feel confusing, unintuitive and difficult to approach.
In this session, I'll share my own attempt to understand Bayesian statistics from the perspective of a healthcare analyst who has spent their career working with more traditional statistical methods. Rather than presenting a comprehensive technical introduction, this talk explores the barriers I encountered and the insights that helped me start making sense of Bayesian thinking.
We'll look at some of the common stumbling blocks, including:
- Navigating unfamiliar terminology such as priors and posteriors
- Understanding conditional probabilities and why they can be surprisingly difficult to reason about
- Overcoming the intimidation of Bayesian formulae and mathematical notation
- Exploring why Bayesian results often seem to contradict our intuition
No prior knowledge of Bayesian statistics is required. The only prerequisite is a willingness to have your intuition challenged.
Bayesian statistics is often presented as a powerful alternative to traditional statistical methods, but for many analysts trained in a frequentist framework, it can feel confusing, unintuitive and difficult to approach.
In this session, I'll share my own attempt to understand Bayesian statistics from the perspective of a healthcare analyst who has spent their career working with more traditional statistical methods. Rather than presenting a comprehensive technical introduction, this talk explores the barriers I encountered and the insights that helped me start making sense of Bayesian thinking.
We'll look at some of the common stumbling blocks, including:
No prior knowledge of Bayesian statistics is required. The only prerequisite is a willingness to have your intuition challenged.