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Advanced Statistics


Admission requirements

Advice: Knowledge of:
Basic statistics: t-test, ANOVA and linear regression.
Basic probability theory: Normal, Binomial and Poisson distributions.
Basic mathematics: Linear algebra at life science bachelor level.

In order to test your statistical knowledge, a test, with accompanying video lectures, will be made available. If the result of the test is unsatisfactory we advise you to follow the bachelor course ”Basis statistics for master students” first; please contact the study advisor for further details.

Contact information

Coordinator: Dr. H.G.J. van Mil


This course discusses probabilistic theory, statistical analysis and statistical modelling in the context of research in the life sciences. Leading concepts in statistics are introduced from the perspective of empirical inquiry and study design. Basic statistics are quickly reviewed and more advanced statistical methods are introduced to deal with data that cannot be analyzed using standard classical methods.

Learning goals

Course objectives:
To understand the probabilistic nature of life processes and its consequences for data analysis in the life sciences. The mastery of a diverse set of methods and their related concepts to allow the student to apply these methods to their data and to critically evaluate the application of statistical methods in literature. To develop a strong intuition on the relation between statistics, data and making decisions regarding data, hypothesis and prediction.

Final qualifications:

  • To set-up experiments taking into consideration elements of experimental design.

  • To independently design a strategy for analyzing the data, both qualitatively and quantitatively, using modern tool in reproducible research.

  • To apply and understand modern methods in statistical analysis and learning.

  • Reflect on some of the normative issues concerning the statistical analysis of complex study designs.

  • A more advanced used of R as a scripting language, including data interactive visualization, advanced analyses, network analyses and simulation.


3 September 2018 – 28 September 2018. Timetable will be communicated through Blackboard.

Mode of instruction

Lectures, tutorials and assignment. Some lectures must be prepared by the students with the use of web lectures and tutorials which might dependent on the Master specialisation followed.

Assessment method

Written exam and an assignment.


Lectures, screen casts, assignments and course blog will be available on Blackboard

Reading list

Set of chapters from the Springer-link library will be published on blackboard. These chapters can be downloaded for free.


Via Usis en enroll in Blackboard

Exchange and Study Abroad students, please see the Prospective students website for more information on how to apply.