Applied statistics

In general, the course aims to reach the following end terms: Knowledge: knowledge on basis statistical data analysis techniques Skills: the student will be able to translate a research question into a statistical problem, which he/she can solve using basic statistical methods. In particular, these methods are related to the analysis of means (e.g. t-tests, ANOVA) and regression analysis.
The student will be capable of performing the data analysis, and of interpreting the results, and he/she will be able to translate these conclusions back to the context of the original research question.

• descriptive statistics (means, medians, percentiles, …);
• some common distributions: normal,
• binomial, multinomial;
• basics of statistical inference:
• confidence intervals and statistical hypothesis tests;
• statistical tests for association in
• contingency tables;
• comparison of 2 means (t-test and mann-whitney test);
• comparison of k means (f-test and
• kruskal-wallis test);
• multiple comparison of means (tukey, bonferroni,..);
• 2-way anova and interaction;
• multiple way anova;
• simple and multiple regression analysis.

After successful completion of this course students are expected to be able to:

• understand the basics of statistical data exploration and statistical inference;
• perform basic statistical data analyses using the software R;
• recognise important problems in the study design/analyses and knows how these may affect the conclusions from the statistical data analysis;
• correctly report the results of a statistical data analysis in a scientific report.

Assumed Knowledge:
A basic knowledge of calculus and probability theory (random variables, probability and distributions) is required.