Hypothesis Testing
As we attempt to make discoveries, we test new hypotheses using experimental data and hope that a sceptical and discerning person would start to believe the claims we make. Hypothesis testing, a branch of statistics, is a step we can rely on to bolster our claims. In this workshop, you will gain a fundamental understanding of common hypothesis-testing concepts and terms, such as null hypothesis, alternative hypothesis, type I error, type II error, p-value, and power.
This is an intermediate workshop in the Biostats series . Prior experience with statistics and experimental design is required. See introductory workshop:
- Presentation: We will go over these slides during this workshop.
- Code to go over: We will work through this code during the workshop.
- Code output: The output of the above code is generated in this file.
- Primer on testing: This is a word document detailing a flowchart guiding you towards simple hypothesis tests. This was prepared with Lennart Mucke and his lab.
Please download the materials for this workshop
You can access these materials remotely at any time and go through them at your own pace. Here's how:
- Go over the slides.
- Work through the code in the R markdown file. You should be able to open it using RStudio installed on your computer. You will need to install the dplyr, onewaytests, pwr, ggplot2, multtest packages to be able to run all the lines of the code.
- As and when you need to test different hypotheses in your research and need guidance, try and consult the primer
- Email Reuben if you have any questions.