Introduction to Statistics and R 2500-EN-CS-SM-01

This is an introductory-level course and part of the “Statistics & Machine Learning” module.

This course provides an introduction to statistics as applied in the social and cognitive sciences, with a strong emphasis on developing an intuitive, “common sense” understanding of core concepts. The focus is on fundamental statistical methods used in experimental and observational research in the social and cognitive sciences. Students will learn how statistics can be used to summarise data, identify patterns, and generalise findings from samples to populations.

The statistical programming language R will be used throughout as both a practical tool for data analysis and a means of illustrating fundamental statistical ideas through simple simulations. By combining theoretical explanations, worked examples, and hands-on exercises in R, students will gain not only technical skills but also the ability to critically evaluate statistical results.

Upon completion, students will be able to carry out common statistical analyses using R, interpret and report results appropriately, and use simulations to deepen their understanding of statistical phenomena. The course equips students with practical tools for handling quantitative data in their own research and prepares them for further methodological training, such as the follow-up course “Advanced Statistical Methods and Models in Experimental Design”.

=== Learning Activities and Teaching Methods ===

In-class exercises and occasional homework, both individual and in groups, with elements of lectures

=== List of Topics/Classes ===

Below is the list of statistical topics:

- Scales of measurement
- Descriptive statistics
- Graphical representation of data and empirical distributions
- Data transformations
- Describing relationships between two variables
- Statistical inference
- Theoretical distributions
- Point and interval estimation
- Key statistical tests
- Statistical power
- Test assumptions and their violation
- Nonparametric tests
- Basics of regression analysis
- Analysis of variance

=== Attendance Rules ===

Attendance will not be checked but retaking in-class assignments will not be possible and missing them might result in a lowered grade.

=== Prerequisites ===

This course provides an introductory-level overview of both statistics and R, and does not require prior coursework in either area. In particular no mathematical background beyond the secondary-school level is expected. On the other hand, as this is a masters-level course, students should be prepared for a potentially steep learning curve, and some familiarity with the command line may be helpful.

=== Academic Honesty ===

Students must respect the principles of academic integrity. Cheating and plagiarism (including copying work from other students, internet or other sources) are serious violations that are punishable and instructors are required to report all cases to the administration.

AI tools are increasingly present in statistical practice. In this course students are welcome to experiment with them, but wise use is expected. In particular, students should not outsource their thinking. They can use AI to learn, but they should always verify and fully understand its output. For example, AI can be used to explain statistical concepts, help understand error messages, or walk through and explain R code. Students remain responsible for verifying AI-generated information and code and must be able to explain and justify any work they submit. The use of AI in assessed work should be transparent and acknowledged where appropriate.