Week 3

Introduction

This week will introduce hypothesis testing - firstly how do we define research questions and hypotheses, and secondly, what are the key statistical tests for evaluating hypotheses.

This week builds on the statistical concepts introduced in week 2. If you’re new to hypothesis testing then I’d recommend having a look at some of the resources below.

Learning Objectives

By the end of this week, you will be able to:

1. Establish a hypothesis for a given research project.
2. Define the Type I and Type II errors.
3. Evaluate a hypothesis using appropriate statistical tests.

Lecture

The lecture slides are available here.

Quiz

To access the quiz on Moodle, please check Moodle page.

Practical

To access the practical:

1. You can preview the practical - which includes the code in both R and Python.
2. You can download the practical here as a python notebook (.ipynb) - ready for you to run the code yourself.

Note

To save a copy of notebook to your own GitHub Repo: follow the GitHub link, click on Raw and then Save File As... to save it to your own computer. Make sure to change the extension from .ipynb.txt (which will probably be the default) to .ipynbbefore adding the file to your GitHub repository.

Further resources

• If you’re new to hypothesis testing then this Khan Academy course might be helpful: https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample
• If you want to refresh on hypothesis testing this chapter is helpful: https://us.sagepub.com/sites/default/files/upm-binaries/40007_Chapter8.pdf - note that is it largely framed in terms of the behavioural sciences, and focuses on statistical tests commonly used in the behavioural sciences.
• Sometimes it can be hard to figure out which statistical test to use. In the lectures we’re only covering a few key tests - but you might find this flowchart or this table helpful for understandign how different tests can be applied.