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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 introductory course Basic statistics for Master students first; please contact the study advisor for further details.

N.B. For the combination master programs (Business Studies, Education and Science Communication) Advanced Statistics is not compulsory. These students can also choose to take Basic Statistics for Master students.

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 design experiments taking into consideration elements of experimental design.

  • To independently design a strategy for analyzing the data both qualitatively and quantitatively.

  • To extent analysis of the deterministic part of the statistical models and to interpret these models from a system theoretic perspective.

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


31 August 2020 – 25 September 2020. Timetable will be communicated through Brightspace.

Mode of instruction

Lectures, tutorials and assignment. Some lectures must be prepared by the students with the use of web lectures and tutorials.

Assessment method

Written exam and a group assignment.
A weight of 75 % for exam and 25% for the group assignment.

Inspection and feedback on the examination

Date: To be announced


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

Reading list

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


Via Usis

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