WEBINAR: Quantifying Uncertainty: Bayesian Data Analysis in Python

It's impossible to collect all the relevant data to answer any particular question, so there is necessarily uncertainty in our analysis. As such, we need to quantify the uncertainty and from that judge our results. Traditional statistical methods (also called frequentist methods) such as hypothesis testing and confidence intervals often don't address this appropriately. For example, we typically want to know the probability that a parameter falls in some range, but this type of analysis is unavailable from a frequentist perspective. Developing a statistical model with frequentist methods is often out of reach for typical data analysts so they are left asking "What test do I apply to this data?"