Statistics in Neurosciences

URL study guide

https://studiegids.vu.nl/en/courses/2024-2025/AM_1276

Course Objective

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

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, the multiple testing problem, Type I and II errors, sampling, and statistical power. Each lecture will provide the theoretical background. The practicals and weekly obligatory assignments will guide students through a series of tailored research problems that they will tackle using the statistical package R. Students will receive hands-on experience in the main steps involved in statistical analyses: from the formulation of hypotheses, selection of the most appropriate test, checking of assumptions, cleaning of data, and running of analyses in R, 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, data analysis assignment, weekly assignments. The weekly 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 the obligatory weekly assignments, a data analysis assignment, and a written exam. The written exam will test your knowledge of the lectures, the practicals, as well as the scientific papers and book chapters. Statistics in Neuroscience is successfully completed if students handed in all weekly assignments and obtained grades of 5.5 or higher for both the exam and the data analysis assignment.

Literature

The literature for the course consists of chapters from a book and several scientific papers. Book:
• Andy Field Discovering Statistics using R, 1st edition (unless the 2nd edition is available by then!), Sage.
- Chapters 1-7, 9, 10, 12, 13, 15 and 19 (19.1-19.6) Additional reading, e.g.,
• Aarts et al 2014 Nat Neurosci; doi:10.1038/nn.3648
• Button et al. 2013 Nature Reviews Neuroscience; doi:10.1038/nrn3475
• Krzywinski & Altman, 2013 Nat Methods; doi:10.1038/nmeth.2738

Target Audience

This is a mandatory course for students admitted to the MScNeurosciences.A selective number of students from the MSc Biomedical Sciences and MScPhilosophy of Neuroscience can participate (ask your master coordinator). Are you enrolled in another program and would you like to participate in this course? Send an e-mail to the course coordinator ([email protected]) before the course starts so that your eligibility can be evaluated.

Additional Information

Coordinator: Sophie van der Sluis ([email protected])

Entry Requirements

Students are assumed to be 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 is provided to give students insight into their knowledge of statistics at the start of the course.

Explanation Canvas

Canvas will be used to share files and upload assignments. Slack will be used for communication with and between students.