Statistics and Reproducibility

Knowledge in statistics is key for understanding and conducting scientific research. It is essential to have basic statistical literacy in order to design experiments, analyze data and eventually present results and draw reasonable conclusions. Ascertainment of reproducibility is important for evidence-based science and therefore of central concern to all scientists. Therefore, Helmholtz Zentrum München sets new training standards in statistics and reproducibility by introducing mandatory courses for graduate students and postdocs.

The following statistics and reproducibility courses are mandatory for all new graduate students and postdocs who received a working contract from 01.01.2019 onwards and will be accredited with HE points.

It is recommended to first participate in the course "Introduction to R" and afterwards in "Introduction to Statistics" since the exercises of the statistics course require knowledge in R. The course "Reproducible and Open Research" does not depend on participation in the other two courses. It is recommended to participate in all three courses during the first year as a graduate student.

Reproducible and Open Research (credit points: 1 HE)

This course provides a broad overview on different aspects of open research and reproducibility. This includes the fields of technical, statistical and computational reproducibility. The participants will be introduced into principles of open research and state of the art techniques of how to enhance the reproducibility of their research. The course consists of lessons on different aspects of open and reproducible research and offers the opportunity to discuss about experiences and expectations on this topic.

Introduction to Statistics (credit points: 8 HE)

This course combines an overview of basic statistical methods with their application in the statistical software package R. Participants will learn how to perform their data analysis using R and interpret their results in a meaningful way. The course covers basic statistical methods, such as descriptive statistics, hypothesis testing, linear regression, and ANOVA. This course does not require any previous knowledge in statistics. However, basic skills in R programming are a prerequisite for the course and can be achieved in the course "Introduction to R".

Introduction to R (credit points: 4 HE)

Statistical analysis can be performed by using a script-based software, which guarantees the traceability and reproducibility of the analysis. The software R is optimal for this, since R provides access to a very broad range of (up-to-date) statistical methods and is also open source. Furthermore, R is the dominating programming language for statistical analyses in academia and is also widely used in industry.
In this course, the participants will be taught how to get started with the statistical software package R and will achieve a basic understanding of how R is working. The course covers basic data structures and routines, such as dealing with vectors, matrices and datasets, and does not require any previous knowledge of statistics or programming. The course consists of lessons how to use R and of hands-on examples with best-practice solutions.


For graduate students who already have appropriate programming skills and/ or knowledge in statistics, mandatory participation in the statistics courses ("Introduction to R" and "Introduction to Statistics") may be waived after consultation with the Core Facility Statistical Consulting. For additional information, please click here.