Research Group Leader

Prof. Dr. Matthias Bethge
Prof. Dr. Matthias Bethge
Phone: +49 7071 29-89017
Fax: +49 7071 29-25015


Secretary: Heike König
Phone: +49 7071 29-89018
Fax: +49 7071 29-25015


Introduction to statistics (Summer term 2009)

Course Content
The application of statistical techniques is ubiquitous in quantitative neuroscience research. Knowledge of the basics of statistic is therefore essential for the correct understanding and interpretation of results published in the literature. Also, publishing and properly reporting the results of one's own research critically depend on statistical skills. This lecture will cover basic concepts important to experimentally working molecular neuroscientists, including descriptive statistics, hypothesis testing, and correlation and regression analysis.

The aim of the lecture is to provide a toolkit for basic statistical analysis and an understanding of important statistical concepts. At the end, students are able to apply the basic techniques to data and acquire additional knowledge on their own. They will also be able to critically assess the statistical techniques used in publications and be aware of the most common pitfalls in the use of statistics.

Location and Time
Friday, 9:15 am - 11 am, Lecture Hall, Graduate School
Important Information
Download information sheet for details about times, exercises and grading.

There will be no lecture on 1.5., 5.6. and 12.6.
Replacement: 8.5.-29.5., 9-12 (9 sharp!) with computer exercises.

For further information, visit the course website at the Graduate School of Cellular & Molecular Neuroscience.

Lecture 1: Introduction & Descriptive Statistics

Lecture 2: Probability theory & Error bars

Lecture 3: Hypothesis testing: T-test

Lecture 4: Paired t-test and Regression

Lecture 5: Correlation coefficient

Lecture 6: Analysis of Variance

Lecture 7: Experimental design and two-factor ANOVA

Lecture 8: Contingency tables and summary

Date due


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Extra material
Mehl et al. 2007, Are women really more talkative than men?

Matlab exercise 1: xls

Matlab exercise 1: m

Matlab exercise 2: m

Matlab exercise 2: mat

Matlab exercise 3: m

Matlab exercise 3: mat

Matlab exercise 3: regression.m

Excerpt from Samuels & Witmer, chapter 12: Regression

Matlab exercise 3: solution
Matlab code for correlation coefficient

Philipp Berens and Alexander Ecker
Last updated: Wednesday, 27.02.2013