Registration deadline: 16 April 2020
AIM OF THE COURSE: Experimental data is always corrupted by noise. Drawing conclusions from data therefore demands for suitable statistical methods. In this course, students will learn basic statistical concepts for analyzing and interpreting their own experimental data.
Block 1: Lectures 1 – 4 + tutorial 1 + lab will cover basic concepts of statistical inference including the t-test, the chi-squared test and linear regression. The three-hour practical will involve real world experimental data and analyzing them with the techniques discussed in the class.
Block 2: Lectures 5 – 7 + tutorial 2 are more advanced (and optional). They will cover random variables and statistical models.
LEARNING OUTCOME: Students will understand the basics of statistical inference. They will be able to apply the acquired skills to analyzing and interpreting real world experimental data.
The number of participants is limited to 20. The course is open for DIPP PhD students and CBG Postdocs.
We will inform you after 17 April 2020 whether a slot in the course could be assigned to you!