http://tinyurl.com/cruk-stats

View the Project on GitHub bioinformatics-core-shared-training/IntroductionToStats

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

- Lecture (pdf)
- Online quiz
- Practical
- Interactive document to record your answers for the group exercise
- Example data for the course

You will need an internet connection in order to run the practicals and examples

- A Course Manual
- Using R for Introductory stats free eBook pdf
- Learning Statistics with R free textbook pdf

- Feedback form for course run on 12th February 2019