IntroductionToStats
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
Aims
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
Learning Objectives
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