This course aims to help you to ask better statistical questions when performing empirical research. We will discuss how to design informative studies, both when your predictions are correct, as when your predictions are wrong. We will question norms, and reflect on how we can improve research practices to ask more interesting questions. In practical hands on assignments you will learn techniques and tools that can be immediately implemented in your own research, such as thinking about the smallest effect size you are interested in, justifying your sample size, evaluate findings in the literature while keeping publication bias into account, performing a meta-analysis, and making your analyses computationally reproducible.
Offered By


Improving Your Statistical Questions
Eindhoven University of TechnologyAbout this Course
A basic knowledge of statistics and research methods is necessary. My previous MOOC 'Improving Your Statistical Inferences' is recommended.
What you will learn
Ask better questions in empirical research
Design more informative studies
Evaluate the scientific literature taking bias into account
Reflect on current norms, and how you can improve your research practices
Skills you will gain
- Computational Reproducibility
- Meta-Analysis
- Experimental Design
- Statistical Inferences
- Philosophy of Science
A basic knowledge of statistics and research methods is necessary. My previous MOOC 'Improving Your Statistical Inferences' is recommended.
Offered by

Eindhoven University of Technology
Eindhoven University of Technology (TU/e) is a young university, founded in 1956 by industry, local government and academia. Today, their spirit of collaboration is still at the heart of the university community. We foster an open culture where everyone feels free to exchange ideas and take initiatives.
Syllabus - What you will learn from this course
Module 1: Improving Your Statistical Questions
One of the biggest improvements most researchers can make is to more clearly specify their statistical questions. When you perform a study, what is it you really want to know?
Module 2: Falsifying Predictions
There is little use in making predictions if you can never be wrong - so how do we make sure your predictions are falsifiable? We discuss why falsifiable predictions are important, and how to make your predictions falsifiable in practice. One important aspect of making predictions falsifiable is to specify a range of values that is not predicted, and we will examine different approaches to specifying a smallest effect size of interest.
Module 3: Designing Informative Studies
If studies are designed to answer a question, you should make sure the answer you will get after collecting data is informative. Instead of mindlessly setting Type 1 and Type 2 error rates, we will learn why it is important to be able to justify error rates, and some approaches how to do so. We discuss the benefits of using your smallest effect size of interest in power analyses, and why learning to simulate data is a useful tool. Simulations can help you to improve your understanding of statistics, enable you to design informative studies, and even ask novel questions.
Module 4: Meta-Analysis and Bias Detection
Regrettably we work in a scientific enterprise where the published literature does not reflect real research. Publication bias and selection biases lead to a scientific literature that can’t be interpreted without taking these biases into account. We will discuss what real research lines look like, and how to meta-analytically evaluate the literature while keeping bias in mind.
Reviews
- 5 stars89.58%
- 4 stars8.33%
- 3 stars2.08%
TOP REVIEWS FROM IMPROVING YOUR STATISTICAL QUESTIONS
Cracking - very informative, nice mixture of modes of learning, and engaging
Great course! Was a pleasure to take it. Thanks, Professor Lakens!
Fantastic state-of-the-art and practical knowledge. It is very useful for researchers at any stage of the scientific career. Thank you, Daniel.
Excellent! Would like only one addition, and that's a more extensive exercise on simulating data with general linear models
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