This e-learning course introduces informative hypotheses: a powerful alternative to traditional null and alternative hypotheses. With a strong emphasis on application rather than formulas, the course offers a hands-on learning experience using R and JASP. You will gain practical skills and conceptual understanding to apply these techniques effectively in your own research.
Since Cohen’s (1994) influential paper “The Earth Is Round, p < .05” in Psychological Bulletin, researchers have become increasingly aware that the traditional null hypothesis (e.g., H₀: μ₁ = μ₂ = μ₃, where μ’s denote group means) rarely reflects their actual expectations. Informative hypotheses provide a more realistic framework by incorporating equality and inequality constraints to formally represent these expectations. For instance:
H₁: μ₁ > μ₂ > μ₃
H₂: μ₁ – μ₂ > μ₂ – μ₃
In recent years, interest has also grown in alternatives to null-hypothesis significance testing.
This course introduces two such approaches for evaluating informative hypotheses:
Both methods quantify the relative support for competing hypotheses by balancing model fit and complexity. For example, if the GORIC(A) weight ratio or Bayes factor comparing H₁ to H₂ equals 5, the data provide five times more support for H₁ than for H₂—suggesting H₁ is the best current description of the population.
In this e-learning course, you will learn what GORIC(A) weights and Bayes factors (and PMPs) are, how to obtain them in R and/or JASP, and how to interpret them.
The course is designed for PhD students, junior lecturers, and researchers who want to deepen their understanding of this methodology and apply it to their own data.
The course emphasizes concepts and examples over formulas, offering a clear and applied learning experience. It is structured as follows:
Before the course:
If needed, please install either the latest version of JASP (https://jasp-stats.org/) or RStudio & R along with the restriktor and bain packages.
Dr. Rebecca Kuiper
PhD students, (junior) lecturers, and junior and senior researchers.
For an overview of all our Winter school courses offered by the Department of Methodology and Statistics please click here.
Learning goals
After completing this course, you will:
One day.
You will receive a certificate upon course completion. Please be aware that this course does not include graded activities, and therefore we cannot provide a transcript of grades.
For staff from the FSBS at UU:
The tuition fee for staff off the Faculty of Social and Behavioural Sciences (FSBS) from Utrecht University will be funded by FSBS.
When registering, please indicate in the “Comment” field that you are FSBS staff at UU, so that the course fee can be waived.
For PhD students from the FSBS at UU:
As a PhD student from the Faculty of Social and Behavioural Sciences (FSBS) at Utrecht University, you can attend up to three Winter or Summer School courses funded by the Graduate School of Social and Behavioural Sciences. Of course, you may choose to take as many other courses as you wish at your own expense, using your personal budget.
When registering, please indicate in the “Comment” field that you are a PhD candidate from the FSBS at UU, so that the course fee can be waived.
Utrecht Summer School does not offer scholarships for this course.