Introduction to Statistical Analysis.
This course provides a refresher on the foundations of statistical analysis. Practicals are conducted using the ‘Shiny’ package; which provides an accessible interface to the R statistical language.
Note that this is not a course for learning about the R statistical language itself. If you wish to learn more about R, please see other courses at the University of Cambridge
- Dominique-Laurent Couturier
- Matt Eldridge
(Acknowledgements: Mark Dunning, Robert Nicholls, Sarah Vowler, Deepak Parashar, Sarah Dawson, Elizabeth Merrell)
During this course you will learn about:
- Different types of data, distributions and structure within data
- Summary statistics for continuous and discrete data
- Formulating a null hypothesis
- Assumptions of one-sample and two-sample t-tests
- Interpreting the result of a statistical test
- Statistical tests of categorical variables (Chi-squared and Fisher’s exact tests)
- Non-parametric versions of one- and two-sample tests (Wilcoxon tests)
We will not cover ANOVA or linear regression here but these are the topics of a more advanced course
After this course you should be able to:-
- State the assumptions required for a one-sample and two-sample t-test and be able to interpret the results of such a test
- Know when to apply a paired or independent two-sample t-test
- To perform simple statistical calculations using the online app
- Understand the limitations of the tests taught within the course
- Know when more complex statistical methods are required
You will need an internet connection in order to run the practicals and examples
This course has received funding from the CRUK Cambridge Centre. If you are researching Cancer in Cambridge please consider becoming a member.