Introduction to Statistics

This course provides the essential skills for conducting sound statistical analysis of scientific data. It is a practical, hands-on course using the R software environment. We focus on the core statistical methods, including descriptive statistics, hypothesis testing, and linear regression. The central goal is to equip you with the ability to independently and critically analyze your own data, select the appropriate methods, and accurately report your findings.

This course does not require any previous knowledge of statistics. The focus of the course is not on programming but on statistic, basic skills in programming with R are a prerequisite for this course. They can be achieved in the course “Introduction to R”.  

Target Audience

Researchers who need to build a solid foundation in practical statistics. This course is designed for those with little or no background in the subject, as well as for those seeking a structured refresher on core statistical methods. (*) 

(*) Waiver Information for Helmholtz Munich Doctoral Students: If you already possess knowledge of the course content (e.g., you are skilled in statistics or recently took a similar course), you may be eligible for a waiver. Please check our homepage for eligibility criteria. If you have questions or your situation is not covered, please contact us at cf-stats-teachingspam prevention@helmholtz-munich.de.

Topics

The course is structured to follow the logical workflow of a data analysis project:

• Foundations: Describing and Visualizing Data
  • Understanding data types and levels of variables
  • Calculating and interpreting measures of central tendency and variability (mean, median, variance)
  • Selecting and creating appropriate graphics to explore data (histograms, boxplots, violin plots)
• From Sample to Population: The Logic of Inference
  • Key concepts of random variables and probability distributions
  • Characteristics of distributions
  • Constructing and interpreting confidence intervals
• Testing Your Hypotheses: Comparing Groups and Effects
  • The core principles of statistical hypothesis testing
  • Applying common tests for comparing groups (t-test, Wilcoxon-test, ANOVA)
  • Justifying the choice of a specific test
  • Addressing the issue of multiple testing
• Modeling Relationships: An Introduction to Linear Models
  • The fundamental concept of linear regression
  • How to build, apply, and interpret linear models
  • Assessing model assumptions and limitations

The focus throughout is on the practical application: when to use each method, how to implement it in R, and how to critically interpret the output. This is no introductory programming course. 

Methods

Each module follows a clear, structured approach. We first introduce the statistical concept and its underlying assumptions. We then demonstrate its implementation in R. You will then apply these methods in hands-on exercises that emphasize practical execution and the correct interpretation of results.

Learning Goals

At the end of this course, you will be able to:

1. Select and apply appropriate methods to describe and visualize datasets.
2. Explain the core principles of statistical inference, including distributions and confidence intervals.
3. Formulate a statistical hypothesis, select the correct statistical test based on data type and study design, and interpret the results.
4. Build, interpret, and critically evaluate simple and multiple linear regression models, including a check of their assumptions.
5. Execute a complete analysis workflow in R, from data exploration and visualization to statistical testing and modeling.

Prerequisites

Basic skills in programming with R (can be achieved in the course Introduction to R). This course does not require any previous knowledge of statistics.

Format

• Duration: 4 days
• Language: English
• This course will be offered either on campus (in person), or online.
• For online courses we use the software Zoom.

Dates and Application

• Courses provided for Helmholtz Munich:
  • You can check the current dates and whether the courses are already fully booked here*.
  • Please read the corresponding FAQ* before applying via the forms of the HR Development department*.
• Courses provided for HIDA:
  • You can check the current dates and whether the courses are already fully booked here.
  • Registrations for these courses are exclusively possible via the provided homepage.

 * Links marked with * are only available for Helmholtz Munich staff.