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Statistics & Data Analysis I


Module name (EN):
Name of module in study programme. It should be precise and clear.
Statistics & Data Analysis I
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Business Psychology, Bachelor, regulation 01.10.2026
Module code: BWP-205
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P470-0011
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
2V+2U (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
5
Semester: 2
Mandatory course: yes
Language of instruction:
German
Assessment:
Project work

[updated 09.11.2026]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

BWP-205 (P470-0011) Business Psychology, Bachelor, regulation 01.10.2026 , semester 2, mandatory course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Susan Pulham
Lecturer:
Prof. Dr. Susan Pulham


[updated 18.09.2026]
Learning outcomes:
After successfully completing this module, students will be able to:
- structure empirical research questions statistically,
- select and interpret appropriate statistical measures,
- apply probabilities and distributions to psychological problems,
- perform hypothesis tests using statistics software,
- interpret effect sizes and confidence intervals,
- critically evaluate statistical results,
- present statistical findings in a way that is tailored to the audience.

[updated 09.11.2026]
Module content:
1. Introduction to empirical data analysis
2. Descriptive statistics and visualization
3. Probability calculus
4. Probability distributions
5. Estimates and confidence intervals
6. Fundamentals of inferential statistics
7. Introduction to statistical decision-making

[updated 09.11.2026]
Recommended or required reading:
Pulham, S. (2011): Statistik leicht gemacht
Georg, L. J.; Georg, S.; Pulham, S. (2026): Einfach nur Wahrscheinlichkeitsrechnung
Field, A.; Miles, J. N. V. (2026): Discovering Statistics Using R
Eid, M.; Gollwitzer, M.; Schmitt, M. (2026): Statistik und Forschungsmethoden
Bortz, J.; Schuster, C. (2017): Statistik für Human- und  Sozialwissenschaftler
Bühner, M., & Ziegler, M. (2017): Statistik für Psychologen und Sozialwissenschaftler

[updated 09.11.2026]
 
[Sun Nov 15 00:34:47 CET 2026, CKEY=wsxdi, BKEY=wpsy, CID=BWP-205, LANGUAGE=en, DATE=15.11.2026]