Statistics in Neurosciences


Course Objective

Students will
• acquire basic statistical knowledge of e.g. research design,
distributions, measurement levels of data, and statistical tests.
• be able to formulate hypotheses, and select the most suited
statistical analysis for a particular experiment or research design.
• be able to understand and critically evaluate scientific articles
• perform statistical tests in R, explore and test the underlying
assumptions, and formally report the results.

Course Content

Statistical data analysis is the process of inspecting, cleaning,
transforming, and modeling data in order to test scientific hypotheses
and answer research questions. The lectures of this course will provide
an overview of quantitative methods that are frequently used in
neuroscience research. These include e.g. correlation, regression,
(paired) t-test, (repeated measures) ANOVA, and multi-level analysis. We
will also discuss concepts like p-values, FDR, Type I and II errors,
sampling, and statistical power. Each lecture will provide the
theoretical background. The practicals and obligatory assignments will
guide you through a series of tailored research problems that you will
tackle using the statistical package R. You will receive
hands-on experience in the main steps involved in statistical analysis:
from the formulation of hypotheses, selection of the most appropriate
test, checking of assumptions, cleaning of data, and running of
analyses, to formally reporting the obtained results. This hands-on
experience is invaluable for the internships in the first and second
year of the Master of Neurosciences, and for your success as an
independent researcher.

Teaching Methods

Lectures, computer practicals, assignments, exam.

Method of Assessment

Written exam, assignments.

The assignments are pass/fail: you need to pass all assignments before
you can participate in the exam.¶

The final grade of Statistics in Neuroscience is based on one assignment
(10%), and a written exam (90%).
The written exam will test your knowledge of the lectures, the
practicals, as well as the scientific papers and book chapters. Of note:
55% of the written exam must be correct to pass the course and obtain a
final grade. Statistics in Neuroscience is successfully completed with a
final grade (90% exam and 10% graded assignment) of 5.5 or higher.

Entry Requirements

It is assumed that you are familiar with chapters 1-5, 7, and 9 of the
book "Discovering statistics using R" by Field, Miles & Field before
entering the course. The first lectures, practicals, and assignments
will provide a short review of these chapters. A short diagnostic entry
test will be provided to give you (and us) insight into your knowledge
of statistics at the start of the course.


The literature consists of chapters from a book and several scientific

• Andy Field Discovering Statistics using R, 1st edition, Sage. -
Chapters 1-7, 9, 10, 12, 13, 15 and 19 (19.1-19.6)

Additional reading:
• Aarts et al 2014 Nat Neurosci; doi:10.1038/nn.3648
• Button et al. 2013 Nature Reviews Neuroscience; doi:10.1038/nrn3475
• Ioannidis 2005 PLoS Medicine; doi: 10.1371/journal.pmed.0020124
• Krzywinski & Altman, 2013 Nat Methods; doi:10.1038/nmeth.2738
• Tsilidis et al 2013 PLoS Biology; doi:10.1371/journal.pbio.1001609

Target Audience

This is a mandatory course for students admitted to the MSc

A selective number of students from the MSc Biomedical Sciences and MSc
Philosophy of Neuroscience can participate (ask your master

Are you enrolled in another program and would like to participate in
this course? Send an e-mail to the course coordinator
( before the course starts so that your eligibility
can be evaluated.

The maximum number of students in this course is N=65.

Additional Information

Coordinator: Sophie van der Sluis (

General Information

Course Code AM_1216
Credits 3 EC
Period P1
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. S. van der Sluis
Examiner dr. S. van der Sluis
Teaching Staff

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Computer lab, Study Group