module 01: basic parametric statistics

module 01: basic parametric statistics

  • module 1 : basic parametric statistics
    07/09/2020 - 11/09/2020
    9:30 am - 11:00 am
    Mon 07/09: 9:30 am - 12:45 pm | online
    Tue 08/09: 9:30 am - 12:45 pm | online
    Wed 09/09: 9:30 am - 12:45 pm | online
    Thu 10/09: 9:30 am - 12:45 pm | online
    Fri 11/09: 9:30 am - 11:00 am | online

Course details

week 1 | register now


The module revisits the foundations for statistical data analysis. Starting from a broad overview of descriptive analysis, we will go over one-sample and two-sample t-tests to comparing more than two groups. Emphasis is on the practical implementation of the theory in the software of your choice (R or SPSS). Real-life datasets from several disciplines are used to illustrate the methods. Participants will have a better understanding of:

  • The numerical and graphical methods to explore, summarize and describe data
  • The use and conditions for applying a one-sample, a two-sample or a paired t-test
  • The use and conditions for applying a global F-test when comparing means of different groups
  • Comparing 2 groups in an ANOVA context
  • Parametric versus non-parametric tests for location: when to use what?
At the end of this module, the participant can perform the above described analysis using computer software (R or SPSS, to be chosen by the student).

Course structure

The module is taught in 3 clustered sessions of 3 x 1.5 hours. Each cluster consists of a session where the problem is situated and the general background is provided, illustrated with real-life datasets from different disciplines. The second session of the cluster is a software session, where examples of the analyses seen in session 1 are shown in the chosen software (R or SPSS, to be chosen by the participant). The last session of each cluster is a hands-on session, where participants are given a real-life dataset and they have to perform the analyses themselves. You will need a laptop with R (or Rstudio) installed on it.


Limited knowledge of basic statistics (meaning: you have had a course in statistics before (for example in your bachelor or master), but you do not know all details anymore. Basic knowledge of the software package of your choice (R or SPSS): importing data and basic data manipulation. Especially for the package R a basic knowledge is necessary!


Dr Ruth Nysen is a post-doctoral researcher at the Center for Statistics (Hasselt University). She obtained a Master degree in Mathematics (KULeuven, 2009), a teaching diploma (CVO LIMLO, 2013) and a PhD in Statistics at Hasselt University (2016). Her research focused on parametric and semi-parametric modeling strategies for censored data. Currently she is involved in several mathematical and statistical courses, mainly in the bachelor programs in the faculty of Medicine and Life Sciences. She is the FLAMES coordinator of Hasselt University.

Cristina Cametti is the Flames coordinator at KU Leuven. She holds a Master degree in Statistics (KU Leuven) and in Crime and Security Sciences (Università Cattolica del Sacro Cuore – Milan). Her interests focused mainly on teaching statistical software courses (R, Python, SAS), on multivariate data analysis and data cleaning techniques.

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