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Open Access Highly Accessed Research article

Innovations in curriculum design: A multi-disciplinary approach to teaching statistics to undergraduate medical students

Jenny V Freeman1*, Steve Collier2, David Staniforth3 and Kevin J Smith1

Author Affiliations

1 School of Health and Related Research, University of Sheffield, Sheffield, UK

2 Learning and Teaching Service, University of Sheffield, Sheffield, UK

3 Independent Educational Consultant, Sheffield, UK

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BMC Medical Education 2008, 8:28  doi:10.1186/1472-6920-8-28

Published: 1 May 2008



Statistics is relevant to students and practitioners in medicine and health sciences and is increasingly taught as part of the medical curriculum. However, it is common for students to dislike and under-perform in statistics. We sought to address these issues by redesigning the way that statistics is taught.


The project brought together a statistician, clinician and educational experts to re-conceptualize the syllabus, and focused on developing different methods of delivery. New teaching materials, including videos, animations and contextualized workbooks were designed and produced, placing greater emphasis on applying statistics and interpreting data.


Two cohorts of students were evaluated, one with old style and one with new style teaching. Both were similar with respect to age, gender and previous level of statistics. Students who were taught using the new approach could better define the key concepts of p-value and confidence interval (p < 0.001 for both). They were more likely to regard statistics as integral to medical practice (p = 0.03), and to expect to use it in their medical career (p = 0.003). There was no significant difference in the numbers who thought that statistics was essential to understand the literature (p = 0.28) and those who felt comfortable with the basics of statistics (p = 0.06). More than half the students in both cohorts felt that they were comfortable with the basics of medical statistics.


Using a variety of media, and placing emphasis on interpretation can help make teaching, learning and understanding of statistics more people-centred and relevant, resulting in better outcomes for students.