Hypothesis Testing and Group Comparisons with R
Covers testing differences between groups in R with appropriate tests, assessing effect size and translating the results into the language of decision-makers.
Training content
- T-test
- ANOVA
- Chi-square
- Non-parametric tests
- Effect size approach
- The logic of multiple comparisons and translating results into the language of decision-makers
At the end of this training, participants
- Will be able to distinguish which test is appropriate for which data structure.
- Will be able to test statistically whether there is a significant difference between groups.
- Will be able to assess not only “significance” but also the size of the difference and its practical impact.
- Will be able to put test results into a standard output template in table and report format.
- Will be able to recognize misinterpretation risks (sampling bias, assumption violations, multiple testing errors) and apply preventive controls.
This will ensure that findings can be confidently carried into institutional decisions.