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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