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 ( An Introduction to Solving Biological Problems with R ).

Authors: Dominique-Laurent Couturier & Mark Dunning
Acknowledgements: Robert Nicholls, Matt Eldridge, 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