This hands-on workshop guides participants in selecting and applying the appropriate statistical tests for their data, introducing key principles of inferential statistics using R. Participants will learn how to perform and interpret common hypothesis tests for widely used models, including correlation, contingency tables, chi-square tests, t-tests, and ANOVA.
Who Should Attend
This workshop is designed for researchers from any discipline who want to understand how to select the right statistical test for their specific context and perform the analysis independently in R. While the content is broadly applicable, examples and exercises will focus on biological and clinical datasets.
By registering, you are consenting to the release of your name and email address to DSCRP for attendance purposes. Full attendance is expected.
Note: Prior experience with R and the command-line interface is essential, as introductory R concepts will not be covered.
Learning Outcomes
- Choosing the appropriate statistical tests based on the data and your research questions
- Conduct inferential statistical analyses using R
- Create plots, figures, and tables of test results using relevant R packages
- Interpret and report findings from commonly used statistical tests
Workshop Topics
- An introduction to hypothesis testing terminology
- Correlation analysis between two continuous variables
- Statistical tests for both categorial and continuous variables
- ANOVA - testing with more than two groups