Introduction to (Bayesian) Hypothesis Evaluation

Course code
Course fee
Course Level

In this one-day course, participants will be introduced to informative hypotheses, which are alternatives for the traditional null and alternative hypotheses, and to the AIC-type criterion GORIC and the Bayes factor, which are alternatives for the p-value. This course is applied, hands-on, and build around concepts and not formulas.

Since Cohen’s (1994) paper “the earth is round, p< .05” in Psychological Bulletin, there is increasing awareness that the null hypothesis, e.g., H0: mu1=mu2=mu3, where the mu’s denote the means in three groups, only rarely represents the expectations that researchers have. Informative hypotheses use equality and inequality constraints to formally represent researcher’s expectation. Two (hypothetical) examples of such hypotheses are: H1: mu1 > mu2 > mu3 and H2: mu1 – mu2 > mu2 – mu3. Since both H1 and H2 may be wrong, it is customary to add Hu: mu1, mu2, mu3 to the set of hypotheses of interest. In Hu there are no restrictions on the parameters of interest. Only if H1 and H2 are better than Hu, they may be valuable.

 Additionally, in the last years there is increasing interest in alternatives for null-hypothesis significance testing.  Two such alternative, (informative) hypothesis evaluation techniques will be introduced: model selection using the AIC-type information criterion GORIC (more specifically, using GORIC weights) and Bayesian model selection (using the Bayes factor). A ratio of GORIC weights and the Bayes factor quantify the support in the data for a pair of hypotheses based on the fit and the complexity of the hypotheses. If, for example, the ratio of GORIC weights or the Bayes factor for comparing H1 with H2 is 5, this means that the support in the data for H1 is 5 times larger than the support for H2. This would imply that H1 is the best available description of the population of interest.

In the workshop, it will be elaborated what the GORIC (weights) and Bayes factor are, how they can be applied, and how they should be interpreted.

This course is tailored to PhD students, (junior) lecturers, and researchers who want to understand this approach and/or apply it for the analysis of their own data. The course is built around concepts and examples, not formulas.

Participants should prepare by downloading the course materials (information will follow[R1] ). Participants have to bring a laptop. Before coming to the course, make sure to either install the latest version of JASP ( and/or the latest version of Rstudio and R (in that case, install 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.

Course director
dr. Rebecca Kuiper


dr. Rebecca Kuiper

Target audience

PhD students, (junior) lecturers and researchers working at a university.

For an overview of all our Winter school courses offered by the Department of Methodology and Statistics please click here.

Aim of the course

After following this course,

- you have some background knowledge of model selection techniques;

- you are able to:

  • think outside of the null-hypothesis box;
  • evaluate informative hypothesis using JASP and/or R;
  • interpret the JASP and/or R output.

Study load

One day


Course fee:
Fee covers
Course + course materials + lunch
Extra information about the fee

- Tuition fee for PhD students from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.

The tuition fee for staff off the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by FSBS

Utrecht Summer School does not offer scholarships for this course.

Contact details

Irma Reyersen | E:


Application deadline: 
Registration deadline
18 January 2024