Statistics in Neurosciences

2018-2019

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
statistically.
• perform statistical tests in SPSS, 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 software package SPSS. 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, presentations.

Method of Assessment

Written exam, assignments, presentation.
The written exam will test your knowledge of the lectures, the
practicals, as well as the scientific papers and book chapters.

Entry Requirements

It is assumed that you are familiar with chapters 1-5, 7, and 9 of the
book before entering the course. The first lectures, practicals, and
assignments will provide a short review of these chapters. The first
lecture will include a short diagnostic entry test just to give you (and
us) insight into your knowledge of statistics at the start of the
course.

Literature

The literature consists of chapters from a book and several scientific
papers.

Book:
• Andy Field Discovering Statistics using SPSS, 4rd edition, Sage. -
Chapters 1-9, 11, 13, 14, and 20 (20.1-20.6)

Additional reading:
• Aarts et al 2014 Nat Neurosci; doi:10.1038/nn.3648
• Button et al. 2013 Nature Reviews Neuroscience; doi:10.1038/nrn3475
• Grabitz et al 2018 J Cogn Neuroscie; doi:10.1162/jocn_a_01192
• 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

Students who have been admitted to the Master of Neurosciences.

Additional Information

Coordinator: Sophie van der Sluis
This course is part of the Master of Neurosciences

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