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Inferential Statistics


Students of the Dutch bachelor’s programme, see Toetsende Statistiek

Entry requirements

This course builds on the Introduction to Methodology and Statistics, and presupposes the knowledge and skills taught there.


Many scientific studies involve taking measurements from study participants. The course Introduction to Methodology and Statistics covered topics like how to set up a study and how to take and present the measurements taken from a sample of participants. However, the final goal of studies is usually not to describe these participants, but to make a general statement. Inferential statistics is about this process, of translating measurements from a sample into a statement about a population. If you want to know if groups of people differ, if an intervention in a workspace has an effect, or if your therapy works, you need to be able to distinguish the real effects from the random variation that results from taking a sample. So we can answer the question: could this just be a coincidence, or is it something real?

In this course, the students’ knowledge of probability theory is refreshed and applied to the statistical tests most frequently used in psychology. Students learn to select and perform the appropriate test in a given situation, understand the reasoning behind them, and what their results mean.

Course objectives

At the end of the course, the student can:
1. understand the concepts of statistical hypothesis testing
2. understand type 1 and type 2 errors, power, effect sizes, and confidence intervals
3. understand common statistical tests for numerical and categorical variables
4. select the appropriate statistical test for a given situation
5. perform statistical tests by manual calculation and interpret the results
6. perform statistical tests using statistical software and interpret the results


For the timetable of this course please refer to MyTimetable



Students must register themselves for all course components (lectures, tutorials and practicals) they wish to follow. You can register up to 5 days prior to the start of the course. The exception here is that first-year bachelor students are assigned and registered for all components in the first semester or academic year by the administration of their bachelor programme. The programme will communicate to these students for which course components and for which period the registration applies.


You must register for each exam in My Studymap at least 10 days before the exam date. Don’t forget! For more information, see the enrolment procedure.
You cannot take an exam without a valid registration in My Studymap.

Carefully read all information about the procedures and deadlines for registering for courses and exams.

Students who take this course as part of a LDE minor or a premaster programme, exchange students and external guest students will be informed by the education administration about the current registration procedure.

Mode of instruction

8 2-hour lectures and 8 2-hour mandatory tutorial sessions.

The lectures

Each course week begins with a lecture to introduce and explain course material. The lectures also cover additional and new topics that are included in the examination. As preparation for the lectures students are required to study the chapters assigned for that week. The lectures will be made available as Weblectures.

The tutorials

On one of the days after the lecture there is a tutorial session. In preparation for these sessions students have to complete a number of mandatory homework assignments, that will be checked by the teacher at the start. These sessions focus on applying the acquired knowledge in practice. Students also learn to work with R, a programming language for statistical data analysis. The material covered in these work group sessions is also included in the examination. Students are required to bring the exercise book (printed or digital) to every session. Attendance is mandatory.

Please note, second year students who have to resit this course will still work with the statistical software IBM SPSS Statistics (instead of R). For students retaking the SPSS exam, attendance is mandatory for the 4 SPSS practicals. These take place in weeks 5,6,7 and 8 of the course.

Assessment method

The assessment consists of two components:
1. A written examination consisting of multiple-choice questions, covering both theory and statistical calculations from the literature, the work group sessions, and the lectures.
2. An R skills test covering the various aspects of students’ skills in working with R as well as in describing and interpreting statistical output.

The final grade is a weighted average of the examination grade (70%) and the grade for the R skills test (30%).

Please note, second year students who have to resit this course will take an SPSS skills test (instead of an R skills test).

The Institute of Psychology uses fixed rules for grade calculation and compulsory attendance. It also follows the policy of the Faculty of Social and Behavioural Sciences to systematically check student papers for plagiarism with the help of software. All students are required to take and pass the Scientific Integrity Test with a score of 100% in order to learn about the practice of integrity in scientific writing. Students are given access to the quiz via a module on Brightspace. Disciplinary measures will be taken when fraud is detected. Students are expected to be familiar with and understand the implications of these three policies.

Reading list and software

  • Howell, D.C. (2014/2021). Statistical Methods for Psychology (Leiden edition). Wadsworth: Cengage Learning. ISBN 978-1-4737-8791-9. E-book, can be ordered online:

  • R (open source software, download for free at

  • R Studio (open source software, download for free at

  • Exercise book Inferential Statistics 2023-2024 (provided on Brightspace)

  • Lecture slides (provided on Brightspace)

  • Please note: Pre-Master students might need additional literature and/or software for using SPSS and will be informed about this by the study adviser or the course coordinator.

Contact information

Sjoerd Huisman
Juan Claramunt (for R)