# Who would you rather date, Bayes Factor or p-value?

Have you ever wondered what is the difference between Frequentist and Bayesian statistics? Why do some people prefer Bayes Factor over p-value for hypothesis testing? How can one come up with prior probability distribution functions for the Bayesian inference? In this tutorial, we will overview the basic concepts of both approaches using inference for a proportion as an example. We will discuss what is a p-value and why do we usually want it to be less than 0.05. Then we will introduce the Bayes Factor and how it can be used in hypothesis testing.

**Outline**:

- Frequentist approach and null hypothesis significance testing
- p-value and some issues related to it
- Confidence intervals
- Bayesian approach
- Bayes Factor
- Prior and posterior distributions
- Credible Intervals

**Key skills**:

After this workshop you will get the idea of:

- The main concepts of Frequentist and Bayesian inference
- Null hypothesis and p-value
- Bayes factor
- Prior and posterior probability functions
- Difference between confidence and credible intervals

**Duration**: 1.5 hours.

**Requirements**: basic probability theory and statistics knowledge (random variables, probability mass/density functions, central limit theorem). Most of the calculations will be done “by hand” along with the Python implementation, so basic Python knowledge will be helpful, but not required.