This practical workshop will help participants to choose and use the appropriate standard statistical test for their data by introducing key concepts of inferential statistics in R. Participants will learn how to compute and interpret hypothesis tests for popular statistical models such as correlation, contingency tables, chi-square test, t-test and ANOVA.
Recommended Participants
Researchers wanting to understand how to choose the right statistical test for the context/condition and how to conduct the analysis by themselves using R. The workshop is relevant for all disciplines, although examples and exercises will be based around biological and clinical datasets.
Prior expertise with R and the command line interface is required to a level equivalent to that provided by the R for Reproducible Scientific Analysis workshop, as the basics of R will not be covered.
Learning Objectives
- Choose the right statistical test appropriate for the data and the research questions
- Carry out inferential statistics in R
- Generate plots, figures and tables of hypothesis tests using specific R packages
- Interpret and report the results of a range of commonly-used statistical tests
Syllabus
- 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