This nine week course (4.5 ECTS, 14 hours per week) provides an introduction to statistical methodology. A number of statistical techniques are covered that are relevant for practical biomedical data analysis.
This course covers the concepts of statistical estimation (point and interval estimation) and testing. It focuses on methods developed for categorical data, in particular binary data, and quantitative data, in particular normally distributed data. The course covers, simple linear regression, correlation, one way analysis of variance, analysis of contingency tables and non-parametric statistics. Furthermore, it introduces (multiple) linear regression and (multiple) logistic regression.
The theory will be presented during web lectures and you will have the opportunity to practice your skills through exercises, discussions, quizzes, assignments and case studies of realistic and real data.
See Course Manual document: Course Manual BOB 2026-2027
Course goals
At the end of the course the student:
1. has knowledge of the role that statistics plays in academic research;
2. has knowledge of basic statistical techniques that are used to analyze data, and knows the conditions under which they are appropriate;
3. has insight in which techniques are applicable in which situation;
4. can apply these techniques by hand and by using statistical software (SPSS and/or R);
5. is able to interpret the results from the statistical analysis;
6. can report these results in the context of the research question.
Although active statistical knowledge is not a prerequisite, we expect some basic knowledge of statistics and mathematics acquired through, for example, courses in biostatistics in the bachelor program or self-study.
It is assumed that you
• are familiar with the concepts of a “population” versus a “sample”
• can interpret data presented in the form of a histogram, boxplot, frequency table, scatterplot or contingency table
• can calculate and interpret from a sequence of numbers: the mean, median, variance, standard deviation, range, interquartile range, standard error of the mean
• are familiar with the concepts probability and probability distributions (in particular the standard normal distribution).
The literature for this course has not yet been finalized. Details about required and recommended reading materials will be communicated to students at a later stage.
This is a fully online course with no fixed schedules for the web lectures. All web lectures are pre-recorded and made available in the online learning environment. The web lectures are provided via the platform and form the foundation for the theoretical explanations within the course.
During the course, you will participate in various learning activities. Statistical theory is explained through web lectures, which you can follow at your own pace and which sometimes include short quizzes to check your understanding. After the lectures, you apply your knowledge in exercises and cases (“Apply your knowledge”), for which examples and answer files are provided, including the use of SPSS and R.
At the end of each learning unit, you complete a quiz to test your knowledge; some questions are similar to those on the exam. In addition, there are discussion activities where you can engage with peers or ask your own questions.
A mandatory component is the group-based case study, in which you analyse a dataset and prepare a detailed report covering the research design, analyses, and interpretation of results. Finally, you reflect on your learning process and how the course aligns with your personal learning objectives.
Transcript of records