5  Pre-reading

If you haven’t already, go through the course overview section. Read the syllabus, especially the part about what the course will cover and what we expect you to learn at the end of it. Then read the learning design section in Chapter 3 and Chapter 2 to get an overview of what types of activities we will do during the course as well as the reasons for them in relation to learning.

Related to the learning design, an important component of learning is repeated exposure to a concept or skill. We want you to read through specific sections of the material beforehand as we’ve found that these particular topics are often more difficult to grasp and understand for beginners. Reading it beforehand will give your brain some time to start processing the information, so that when you see it again during the course, it will be easier to understand and learn. During the course, you will read these sections again and we will explain them verbally as well, to further reinforce the concepts.

Because our main learning outcome is statistical litteracy, we will use tools like R that is the foundation for most modern statistical workflows. Without a way to easily and quickly reproduce analyses, statistical work becomes daunting and inefficient. Using R is the most common way to write statistical analyses within the health sciences.

So, please read about R and how to set it up in Chapter 6, Chapter 7, and Chapter 8. R and its associated computer code will be the main tool for the course. Learning how to program requires a change in how you see and interact with your computer in profound ways. Try not to underestimate R and what it can do for you in order to learn quicker during this course.

Lastly, briefly read through the Appendix B so you are familiar with what you will do at the end of the course. You’ll be formally introduced to the projects later, but we’ve found that students prefer and want to be aware beforehand on what is expected of them during the project work.

Tip

To keep track of which sections to read, here is a list:

  1. Chapter 1
  2. Chapter 3
  3. Chapter 6,
  4. Chapter 7,
  5. Chapter 8,
  6. Appendix B

5.1 Cheatsheets

RStudio provides some great cheatsheets that are fantastic resources and a great reference to have readily available, either on your computer or printed out. We suggest that you download at least these following cheatsheets and have them available either digitally or printed out during the course, especially for when you do the exercises and team project work:

5.2 Using AI

We provide solutions to the exercises, but a solved exercise is not the goal — learning is.

Use ChatGPT and other chat models responsibly. They can help you understand, question, and explore, but they can also give you convincing nonsense.

Do not outsource your thinking.

The time to make mistakes, struggle, and learn from them is now — not when you are responsible for patients, colleagues, or decisions in the workplace.

5.2.1 Examples:

Legitimate use cases

  • Ask for an explanation of a statistical concept you do not understand.
  • Ask for a hint when you are stuck rather than asking for the answer.
  • Make it explain errors in R instead of generating the code.
  • Compare your own solution with a suggested solution and identify where they differ.
  • Ask the model to challenge your interpretation of a result or point out assumptions you may have missed.
  • Use it after attempting an exercise to clarify mistakes and deepen your understanding.

Poor use cases

  • Copying the solution without first attempting the exercise.
  • Asking the model to solve an exercise so you can submit or reproduce the answer.
  • Using a model’s statistical reasoning without checking whether it is correct.
  • Letting the model choose a statistical method you cannot explain yourself.

Further resources on responsible use of AI

You are responsible for what you submit — and for what you learn.