cookieImage
2025-2026 / PSYC5895-1

Descriptive and inferential psychostatistics (part 1)

Duration

30h Th, 30h Pr

Number of credits

 Bachelor in psychology and education : speech and language therapy6 crédits 
 Bachelor in psychology and education : general6 crédits 

Lecturer

Etienne Quertemont

Language(s) of instruction

French language

Organisation and examination

Teaching in the first semester, review in January

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

This course offers an introduction to the main statistical methods used in psychology and speech-language therapy. It covers fundamental concepts such as:

  • types of variables,
  • measures of central tendency and dispersion,
  • the normal distribution,
  • probability calculations,
  • confidence intervals,
  • hypothesis testing and inferential statistics.
It also includes:

  • the chi-square test for categorical data,
  • the Student's t-test for paired or independent samples,
  • non-parametric tests,
  • correlations and linear regressions.
The teaching approach is strongly practice-oriented: emphasis is placed on data analysis and solving concrete exercises related to situations commonly encountered by researchers. The goal is to enable students to master essential statistical tools for understanding and interpreting data in psychology and speech-language therapy.

Learning outcomes of the learning unit

By the end of the course, students will be able to:

  • Analyze data from scientific studies in psychology, speech-language therapy, or educational sciences using basic statistical techniques, and accurately interpret the results.
  • Read and understand the findings of scientific studies that include elementary statistical analyses.
  • Compute the statistics required for administering standardized tests in psychology and speech-language therapy (e.g., Z-scores).
  • Adopt a critical perspective on the generalization of conclusions based on sample measurements.

Prerequisite knowledge and skills

The course requires a good knowledge of basic mathematics

Planned learning activities and teaching methods

Practical online exercises, available through eCampus, accompany each chapter of the course and provide students with immediate feedback on their answers. Additionally, three sets of summary exercises are offered online to reinforce learning.

Mode of delivery (face to face, distance learning, hybrid learning)

Blended learning


Additional information:

The theoretical lessons are organised face-to-face, whereas the practical exercices are online.

Course materials and recommended or required readings

Platform(s) used for course materials:
- eCampus
- MyULiège


Further information:

Course materials are provided in the form of a theoretical syllabus and an exercise syllabus. All resources are primarily available through the eCampus platform.

Exam(s) in session

Any session

- In-person

written exam ( open-ended questions )


Additional information:

An open book written exam is organized at the end of the course. The questions essentially involve problem solving and data analysis.

Work placement(s)

Organisational remarks and main changes to the course

Contacts

Teacher: E. Quertemont, Tel: 04/366.21.05, equertemont@uliege.be
Practicals: V. Didone, Tel: 04/366.22.63, vdidone@uliege.be

Association of one or more MOOCs