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 are, how to apply them in R and/or JASP, and how to interpret their outcomes.
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.
Please note that there are no graded activities included in this course. Therefore, we are not able to provide participants with a transcript of grades, only a pass.
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:
interpret and report the output from these analyses
One day.
The tuition fee for staff off the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by FSBS.
PhD students from the Faculty of Social and Behavioural Sciences at Utrecht University have the opportunity to attend three Winter/Summer School courses funded by the Graduate School of Social and Behavioural Sciences. Additionally, they may choose to take as many courses as they wish at their own expense from their personal budget.
Utrecht Summer School does not offer scholarships for this course.