About the class
Course Information
Welcome to the class!
This class is part of the module scientific research method and constitutes its practical part. As the title of the module suggests, we will work with scientific methods, specifically through the lens of statistics. Not only is statistics a fascinating field, but it is also an essential part of scientific education. Yet, don't expect a traditional statistics lecture. This is a class where you will dip your toes into the most relevant concepts and get equipped to avoid a jump-scare when you encounter statistics in the wild!
The concept of the class
The class combines three key components.
You will know answers to questions, such as:
- what is a statistical test and why is it needed?
- what is important for choosing an appropriate statistical test?
- what exactly is a hypothesis?
- what is a p-value and what does and doesn’t it represent?
- what are all these concepts: two-tailed test, paired-test, ANOVA, confidence interval, effect size and more!
You will be introduced to results from research papers and try to replicate them or follow their analysis to better understand how scientific questions are answered with the help of statistics.
You will have a chance to train better writing, presenting and reading skills!
The structure of the class
To help you develop basic statistical skills, the class follows a three-block structure. You will start with guided activities and gradually become more independent as the semester progresses.
Block 1: Things Aren't As Simple As They Seem
The highlight of this block is that we will zoom in on a single research figure and dissect it to understand what every number and element actually means!
Block 2: Answering Questions With Statistics
Will you get the same answer as the researchers? In this block, we will answer research questions using real data from published papers. For example, we will explore questions such as whether ChatGPT makes people more creative.
Block 3: The Deep End Of The Pool
This is where you start becoming more independent! We will explore statistical concepts that require a closer look and aren't always as straightforward as they seem. You will also get the opportunity to apply what you've learned by conducting an additional analysis on your own.
Class Principles
- This website can be your go-to place for the class. However, all communication and submissions will happen via Moodle.
- You will receive plenty of feedback at every stage.
- The goal is to learn a lot and complex concepts at that but without getting overwhelmed!
- A lot of effort goes into preparing this class, so I expect you to participate in good faith!
- The lessons are designed not to overwhelm you. In return, I expect genuine effort rather than AI agents parsing through carefully prepared materials on your behalf!
AI use
This course operates in the yellow tier of AI use. This means that some uses are allowed, while others are prohibited.
Specifically, AI is allowed for:
- Code: You can generate code if the functionality is pre-specified by you and checked. Example: write pseudo-code and the logical steps and write the Python implementation with AI. If you have questions you can ask AI for hints but don't just outsource it all to it. Not permitted: giving the assignment to AI and copying the generated code!
- Proofreading (in the sense of advanced Grammarly): Use AI to check your language, spelling and syntax. Ask for improvements to individual phrases rather than generating entire passages and copying them into your work.
- Getting some "peer feedback": Ask AI to identify potential weaknesses in your writing, then make the improvements yourself. Treat its suggestions critically rather than accepting everything it says. And don't accept AI suggestions of "If you want, I can also [do this or that for you]!
Consequences of policy violation: You will get feedback regarding permitted AI use during the assignments and I will help you to find the right scope of use. However, if you don't follow the recommendations, especially after clarifications, your submissions will not be accepted and you may fail the class. That's not my goal though, so when in doubt, ask!
Formal requirements
- Required knowledge: basic Python programming skills
- Module: 39-M-Inf-SRM Scientific Research Methods
- Class type: practical seminar
- Credits: 5CP : 4 practical work + 1 examination
- Workload: 120h : 30 contact time + 90 independent work
- Assignments for practical work: 4 in total: 1/3 for Block 1, 2/4 for Block 2, 1/1 for Block 3
- Examination: short presentations + a written report
- Grading: none (but you will get individual feedback)
For more, see the section: Frequently asked questions