Below is information on statistical teaching in the School of Medicine, Medical Sciences and Nutrition and the School of Biological Sciences.  Please also visit the feedback page to see comments from previous students on some of our courses.

School of Medicine, Medical Sciences and Nutrition

Members of the Team co-ordinate and/or contribute teaching for the groups specified below.  If you are interested in our SPSS and Medical Statistics courses, please visit the course page.


MBChB Medicine

  • Medical Module years 3/4 Specialist Clinical Practice 1A (ME4804) / Specialist Clinical Practice 2 (ME4403)
  • 3M Critical Appraisal (SSC 3)
  • Medical Module years 1-3 (Online materials)

Postgraduates Taught (MSc)

  • Applied Statistics (PU5017)
  • Systematic Reviewing (PU5018)
  • Applied Statistics (Online PU5522)
  • Evidence-Based Health (Online PU5523)

Postgraduates Research (PhD)

  • Basic Statistics (3 day generic statistics course) for PhD students from across the University. This course is run as part of the Researcher Development training program.

University/NHS staff

  • GP Registrars (for NHS Education)
  • Foundation Year Academics (MT5026)
  • Introduction to SPSS
  • Introductory Medical Statistics
  • Intermediate Medical Statistics 1: analysis of measured health outcomes
  • Intermediate Medical Statistics 2: analysis of binary health outcomes
  • Intermediate Medical Statistics 3: an introduction to the analysis of time-to-event outcomes

The coordinator of all teaching involving the Medical Statistics Team is Dr David McLernon

School of Biological Sciences

The following courses are currently co-ordinated by and/or contributed to by Dr Alex Douglas and Thomas Cornulier.


  • Biodiversity Field Course (BI29Z3)
  • Statistical Analysis of Biological Data (BI3010)

Postgraduates Taught (MSc)

  • Experimental Design and Analysis (BI5009)
  • Statistics for Complex Study designs (BI5010)
  • Introduction to Bayesian Inference  (BI5505)
  • Population and Community Ecology (ZO5304)

Postgraduates Research (PhD)

  • An Introduction to R
  • Introduction to Statistical Modelling with R

University Staff

  • An Introduction to R