Hypothesis Testing 3.0

Course code
Course fee (excl. housing)
Advanced Bachelor

Unfortunately, this course is cancelled this summer due to COVID-19 regulations. We expect to be able to offer this course again next year.

This course discusses the evaluation of theory-based hypotheses using p-values, the Bayes factor, and information criteria. There will be attention for contemporary phenomena, like publication bias, questionable research practices, the replication crisis, the statistical evaluation of replication studies and studies in which multiple data sets are used to evaluate the same research question. The course will be non-technical in nature, targeted at students and researchers who want to use the approaches for the evaluation of their own data.

The evaluation of hypotheses is a core feature of research in the behavioral, social, and biomedical sciences. In the last decade, there has been a lot of attention for inappropriate use of hypotheses testing by journals (publication bias) and authors (questionable research practices) as the main causes of the replication crisis (see, for example, Open Science Collaboration, 2015). This course will use different perspectives (classical, Bayesian, information theoretic) to teach participants in a non-technical manner the theory-based hypothesis evaluation and the appropriate application of hypothesis evaluation. The course is targeted at students and researchers who want to learn how to evaluate theory-based hypotheses.

During the first day of the course, null-hypothesis significance testing (Kuiper and Hoijtink, 2010), questionable research practices, publication bias, and the replication crisis will be discussed. This day will also be used to give a hand-on introduction to statistical analyses using R.

During the second day, hypothesis evaluation using the Bayes factor will be discussed (Hoijtink, Mulder, Van Lissa, and Gu, unpublished). There will, among others, be attention for Bayesian error probabilities, Bayesian updating, the use of Bayesian hypothesis evaluation for the evaluation of replication studies, and combining evidence from multiple studies addressing the same research question (Kuiper, Buskens, Raub, and Hoijtink, 2013).

The third day of the course will start with an introduction of information criteria (i.e., AIC and its generalization called the GORIC) and a brief repetition of theory-based hypotheses. Subsequently, it will be elaborated how the GORIC can be used to evaluate null, alternative, and theory-based hypotheses. There will be attention for GORIC weights, which have a nice interpretation. This day will, like Day 2, be concluded with examples of replication research and combining evidence from multiple studies in which informative hypotheses play an important role.

During the morning of the fourth day, a debate between three groups (classicists, Bayesians, and information theorists) will be organized in which the (dis)advantages of the approaches presented in this course will be discussed. In the afternoon, there is a lab meeting in which you have the opportunity to analyze your own data (or data provided by the lecturers) with the approaches taught in this course.

Participants are requested to bring their own laptop computer. Software will be available online.

Among our Methodology and Statistics postgraduate courses, there is one more course that address Bayesian statistics: Applied Bayesian Statistics.

The differences between theses courses are:

  • Applied Bayesian Statistics gives a broad overview of Bayesian statistics, with attention to the statistical theory (using formulas) and the application of Bayesian concepts to mean comparisons and parameter estimation in linear regression models.
  • This course addresses classical hypothesis testing, Bayesian model selection and model selection using information criteria. The focus is on the conceptual level (there will be hardly any formulas) and on the application of them in the data-analysis.

Please note that there are no graded activities included in this course. Therefore, we are not able to provide students with a transcript of grades. You will obtain a certificate upon completion of this course.

Download the day-to-day programme (PDF)

Course director

Dr. Rebecca Kuiper


Prof. dr. Herbert Hoijtink and Dr. Rebecca Kuiper

Target audience

The course will be non-technical in nature, that is, it is targeted at students and researchers who want to use the approaches presented for the evaluation of their own data. The participants can come from a variety of fields. For example, sociology, psychology, education, human development, marketing, business, biology, medicine, political science, and communication sciences. A maximum of 24 participants will be allowed in this course.

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

Course aim

After attending the course, you will understand the classical, Bayesian and information theoretic approaches to hypothesis evaluation. You will obtain the practical skills necessary to evaluate hypotheses using these approaches. Moreover, you will learn to evaluate replication studies and to combine the evidence from multiple diverse studies.

Study load

4 full days: - Days 1, 2, and 3 consist of lectures including small hands-on-your-own-laptop exercises. - Day 4 contains an elaborate discussion meeting and a lab meeting in which participants will practice on their own data (with their own laptop)


Course fee:
Course + course materials
Housing fee:

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

Housing through: Utrecht Summer School.


There are no scholarships available for this course.

More information

Irma Reyersen | E: ms.summerschool@uu.nl

Recommended combinations
Applied Bayesian Statistics (April)


Application deadline: 19 June 2020

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