Improving statistical literacy

About us

We are a group of statisticians from academia, health trusts and industry. The key aims of this working group are:

  1. To enhance the communication of statistics and improve statistical literacy throughout the NIHR.
  2. To bring together a network of interested individuals.


There is evidence of suboptimal statistics within medical research and evidence also that this can impact upon patients.  There is evidence of delays in embracing novel statistical methodology and this too can impact upon patients in that the newer methods might deliver results with fewer patients and at a faster rate.

General Resources

Useful Links

Features a range of learning resources and workshops on statistics (among many other topics). A simple search e.g., ‘statistics’ should bring up relevant materials. A number of these are open access.

“I finally understand the difference between MCAR and MAR after reading this paper! The layout is different to anything I’ve seen in the published literature which makes it very easy to read and approachable.”

For researchers in eyes and vision, there is the series that has been put together by the Ophthalmic Statistics group



  • An Introduction to Medical Statistics by Martin Bland

“Good introductory text – accompanying workbook is great and contains answers.”

  • Essential Medical Statistics (Second edition), by Betty Kirkwood and Jonathan A.C. Sterne

“Good introductory text – helpful for those who don’t like mathematical formulas.”

  • Medical Statistics at a Glance, Second edition by Aviva Petrie and Caroline Sabin

“Good introductory text and the flowchart at the back of the book is very helpful!”

  • Oxford Handbook of Medical Statistics by Janet Peacock and Philip Peacock

“Excellent book covering a wide variety of topics. It’s always the first book I pick up when I want to double check something.”

  • Practical Statistics for Medical Research by Doug Altman
  • Statistical Questions in Evidence Based Medicine by Janet Peacock and Martin Bland

“Excellent book that encourages the reader to critically appraise published papers and contains answers to the questions. It’s also a good revision tool.”

  • Know Your Chances: Understanding Health Statistics by Steven Woloshin, Lisa M. Schwartz, and H. Gilbert Welch.

“A book about how to understand medical health information through statistics.”

Resources for Statisticians

Useful Links

“Explains latent class trajectory analysis from first principles and includes relevant published examples.”

“Contains excellent annotated output, along with the relevant code, for various statistical techniques. The annotated output comes from a variety of statistical analysis software such as Stata, SAS, SPSS, Mplus and R.”



  • Discovering Structural Equation Modeling

“A good first guide into the world of Structural equation Modelling. Should ideally be used in conjunction with another introductory text as its focus is applying the techniques.”

  • Multilevel and longitudinal modelling using Stata (Volumes I and II, Third Edition) – by Sophia Rabe-Hesketh & Anders Skrondal

“A great text for explaining hierarchical modelling theory, as well as thorough interpretation of the code and output.”

  • Negative Binomial Regression (Second edition), by Joseph M. Hilbe

“Best text for negative binomial regression it includes detailed appendices which contain excellent interpretation of interactions.”


Contact us

Please get in touch if you come across a helpful statistical resource or if you would like to join our group, please email

Section leaders

Group Co-leader, Improving Statistical Literacy

Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford.

Group Co-leader, Improving Statistical Literacy

Centre for Biostatistics, University of Manchester

Working group members

Birmingham University

Queen Mary University of London

University of York

University of Manchester

Mac Clinical Research

Imperial College, London

Little Havens Children’s Hospice, Essex


King's College London

University of Sheffield

King's College London

Queen Mary University of London

Royal Marsden NHS Foundation Trust

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