Bayesian Hypothesis Testing Guide
This page will serve as a guide for those that want to do Bayesian hypothesis testing. It's a collaborative effort to try and gather as many procedures(and code) possible that currently exists in Bayesian statistics. The goal is to create an easy to read, easy to apply guide for each method depending on your data and your design. Most of these methods can be applied in scientific disciplines such as Social sciences, Psychology, HCI and others. The philosophy behind this guide is to always keep things simple. Just as we don't ask for my visitors on any website to understand HTTP requests, the same should apply for someone that wants to perform Bayesian statistics. You only need to know what your input is and how to interpret the output. Therefore, the emphasis here is taken away from the mathematical aspects of Bayesian statistics in favor of the actual application.
Note: Some of the terms used in this guide may have been altered in order to match those from the traditional NHST procedures. This was made so that people that already use NHST techniques can easily find the Bayesian equivalents. In addition, terms from traditional hypothesis testing and survey designs won't be explained here. Bayesian definitions will be explained here when appropriate but you can also get a basic introduction here as well as here.
Experimental Designs[edit | edit source]
- Bayesian t-test hypothesis testing for two independent groups(For interval values that are normally distributed)