Bayesian Hypothesis Evaluation using JASP or R

Course code
Course fee
Course Level

In this one day course participants will be introduced to informative hypotheses that are alternatives for the traditional null and alternative hypotheses, and, to the Bayes factor and GORIC 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: m1=m2=m3, where the m’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: m1 > m2 > m3 and H2: m1 – m2 > m2 – m3. Since both H1 and H2 may be wrong, it is customary to add Hu: m1, m2, m3 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 for alternatives for null-hypothesis significance testing.  One such alternative, (informative) hypothesis evaluation using the Bayes factor, will be introduced. The Bayes factor quantifies the support in the data for a pair of hypotheses based on the fit and the complexity of the hypotheses. If, for example, BF12 = 5 and BF1u =10, this means that the support in the data for H1 is 5 times larger than the support for H2 and 10 times larger than for Hu. This would imply that, currently, H1 is the best available description of the population of interest. Another alternative is an information criterion called the GORIC that also evaluates informative hypotheses based on their fit and complexity and has an interpretation analogous to that of the Bayes factor.

In the workshop it will be elaborated what the Bayes factor and GORIC are, how it can be applied and should be interpreted. There will be attention for updating (an alternative for power analysis), (conditional) error probabilities, and limitations of the approach.

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, formulas will not make an appearance.

Participants should prepare by downloading the course materials (including tutorial papers) from the very bottom of the Bain website at under Winter School: Bayesian Hypotheses Evaluation Using JASP. Participants have to bring a laptop with the latest version of JASP installed ( Alternatively you can also use Rstudio, and R, and in that case install the bain and goric package before coming to the course.

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/fail.

Course director
Prof. dr. Herbert Hoijtink


  • Prof. dr. Herbert Hoijtink
  • Dr. Rebecca Kuiper

Target audience

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

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

Study load

One day


Course fee:
Fee covers
Course + course materials
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.

Utrecht Summer School does not offer scholarships for this course

Contact details

Irma Reyersen | E:


Application deadline: 
Registration deadline
22 January 2023