ECTS credits


Number of hours: Lectures + Seminars + Exercises

15 / 15 /30

Course objectives

The course aims to acquaint students with a wide range of statistical methods and techniques used in cognitive sciences.

At the end of the course, students will be able to explain the logic of the statistical approach in cognitive science. In addition, students will be able to choose and apply appropriate descriptive and inferential methods in the process of statistical data analysis and interpret and critically evaluate the results of statistical analyses. By the end of the course, students will have acquired practical knowledge on data analyses and usage of statistical software.

Enrolment requirements and/or entry competences required for the course


Learning outcomes at the level of the programme to which the course contributes

  • Integrate insights, methods, and levels of analysis across different disciplines into a unified framework for understanding the human mind and cognition in general.
  • Critically evaluate cognitive science findings and synthesize information to be employed in a collaborative professional environment.
  • Participate in data-driven innovation projects and apply appropriate data science tools.
  • Employ cognitive science insights in developing innovative, human-friendly and sustainable technological solutions.
  • Design and conduct an interdisciplinary research project in cognitive science.

Course content (syllabus)

  • Discrete and continuous variables; Probability mass and density functions.
  • Central limit theorem and normal distribution.
  • Measures of central tendency; Measures of variability.
  • Sample and population; Random samples and bootstrapping.
  • Interval estimation; Confidence intervals.
  • Null-hypothesis testing; t-test.
  • Effect size and statistical power.
  • One-way ANOVA; Factorial ANOVA.
  • Repeated measures ANOVA; MANOVA; ANCOVA.
  • Chi-sqaure test.
  • Pearson's correlation; Spearman's correlation.
  • Regression and partial correlation.
  • Contingency coefficients; Intraclass correlation; Other measures of association.
  • Permutation tests and non-parametric tests.
  • Introduction to multivariate analyses.

Student responsibilities

Class attendance.

Required literature

  • Spiegelhalter, J. D. (2019) The art of statistics: learning from data. Pelican, Penguin Books.
  • Rohatgi, V. K., & Saleh, A. K. M. E. (2015). An introduction to probability and statistics (3rd edition). John Wiley & Sons, Inc

Optional literature

  • Lee, P. M. (2012). Bayesian statistics: An introduction (4th ed). Wiley.