Summary and Schedule
This is a new lesson built with The Carpentries Workbench.
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Overview and motivation |
What is a hypothesis test used for? What are the general steps of a hypothesis test? |
| Duration: 00h 05m | 2. Our first test: Disease prevalence |
What is the null distribution? How can we use the null distribution to make a decision in hypothesis testing? How can we run a hypothesis test in R? |
| Duration: 00h 22m | 3. One-sided vs. two-sided tests, and data snooping |
Why is it important to define the hypothesis before running the
test? What is the difference between one-sided and two-sided tests? |
| Duration: 00h 32m | 4. Errors in hypothesis testing | What errors can occur in hypothesis testing? |
| Duration: 00h 35m | 5. One-sample t-test |
What is a one-sample t-test? Why is \(t\) a useful test statistic? |
| Duration: 00h 57m | 6. The distribution of t according to the Central Limit Theorem |
What is the central limit theorem? What does it predict for the distribution of \(t\)? |
| Duration: 01h 04m | 7. The distribution of t in practice |
Is the t-statistic normally distributed? What is the t-distribution? |
| Duration: 01h 16m | 8. The two-sample and paired t-test |
How is the one-sample t-test extended to two samples? What is pairing? What is pairing good for? |
| Duration: 01h 28m | 9. Interpreting p-values | What are common mistakes when interpreting p-values? |
| Duration: 01h 34m | 10. Summary and practical aspects | |
| Duration: 01h 37m | 11. References | |
| Duration: 01h 37m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
Learning goals for this lecture
- Understand the common principles behind statistical tests
- learn to spot common pitfalls
- Understand t-test, chi-square test, and Wilcoxon test
- Identify and deal with multiple testing scenarios
- Perform hypothesis testing and p-value adjustment in R
Prerequisites
- Data handling and visualization using the
tidyversein R (or completing this tutorial) - Basics on statistical distributions (covered in this lecture)