Wednesday: 08:15 - 09:45, weekly (from 19/10/22), Location: A06 0-001 Dates on Friday. 10.02.23 09:00 - 10:00, Location: A07 0-030 (Hörsaal G) The class in only open for Neurocognitive Psychology, Neuroscience and PhD students, NOT for Biology students.
Friday: 12:15 - 17:45, weekly (from 21/10/22) Dates on Friday. 14.10.22 12:15 - 17:45 This course is designed for students who are completely new to the world of statistics and for those who have the feeling that many statistical concepts they learned about earlier are not present to them anymore. Relying on theoretical input and applied exercises, this interactive lecture covers all those topics that need to belong to students’ procedural knowledge in order to be able to follow the topics covered by the Psychological methods module.
Course contents
• Empirical research, variables and scales
• Statistical parameter
• Graphical data visualization
• Probability theory
• Probability distributions
• Statistical sampling
• Hypothesis testing
• Testing hypothesis on differences
• Correlation
• Simple linear regression
Additonal voluntary tutorial for the multivariate statistics lecture.
If you are from another study program, please contact the teacher.
Hinweise zum Modul
Prerequisites
Enrolment in Master's programme Neurocognitive Psychology. Module psy110 is only relevant for students who started their studies before winter term 21/22. (All other students study modules psy111 and psy112.)
Module examination
The module will be tested with an oral exam (20 min).
Required active participation for gaining credits:
attendance of at least 70% in the seminars (use attendance sheet that will be handed out in the beginning of the term).
Students will acquire basic knowledge in planning empirical investigations, managing and
understanding quantitative data and conducting a wide variety of multivariate statistical
analyses. They will learn how to use the statistical methodology in terms of good scientific
practice and how to interpret, evaluate and synthesize empirical results from the perspective of
statistical modeling and statistical learning in basic and applied research context. The courses in
this module will additionally point out statistical misconceptions and help students to overcome
them.
Competencies: ++ interdisciplinary kowledge & thinking
++ statistics & scientific programming
++ data presentation & discussion
+ independent research
+ scientific literature
++ ethics / good scientific practice / professional behavior
++ critical & analytical thinking
++ scientific communication skills
+ group work
