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Inferential Statistics

At the end of the course, candidates will be able to, in general terms:

More specifically students will be able to:

Assumed previous knowledge

It is assumed students are very well versed in the distinction between units and variables; the measurement levels of variables (dichotomous, nominal, scale); the main ‘statistics’ describing variables (‘mean’, ‘standard deviation’ and ‘variance), the ‘standardization’ of variables, and with the (standardized) normal distribution (and the associated ‘empirical rule’). These topics are covered in the course Research Methods and Descriptive Statistics.


In this course the basic notions of data analysis are introduced that would allow to make inferences about populations on the basis of a randomly sampled data set. The course uses the regression (or ‘linear’) model as the basic skeleton and in this context introduces confidence intervals and tests. In addition, it familiarizes students with the logic and implementation of some non-parametric statistical analyses (methods that do not use a concepts like ‘the mean’ and ‘variance’). Usage of these methods is illustrated using research examples. The software used in both teaching and in the assessment is R for statistics.

Assignments and exams

The assessment in this course consists of some assignments and two written exams.
The assignments count for 20% of the final grade, while the exams both count for 40% of the final mark.
The minimum mark for an exam must be at least 5.0.
The individual assignments are mandatory. They are only accepted as a ‘valid attempt’, if you try to seriously answer all questions in the assignments and hand in the assignment before the deadline. There is no minimum grade for the assignments though. If you do not make all assignments and hand them in before the deadline, you will not be allowed to take part in the written exams.
The final grade of the course should be at least a 5.5.

For both partial exams a retake will be offered.
For the assignments there is no graded retake, although you are allowed to repair the omission to hand in a valid assignment by handing in a repair assignment for which you will not get points. 

  • Programme is for


  • ECTS


  • Location

    at the campus

  • Schedule

    Please contact the lecturer for the actual schedule (about 20 sessions week 18 - 27, 2024)

dr. H. van der Kolk (Henk)
Associate Professor
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Inferential Statistics
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