INTRODUCTION TO STATISTICS FOR EVIDENCE-BASED PRACTICE - 2025/6

Module Overview

This is a course for health and social care professionals who want to strengthen their statistical skills and their ability to critically appraise quantitative evidence. The purpose of this module is to provide students with the key concepts and principles of statistics, and quantitative design, as applied to healthcare and social research. The module will guide students through how to develop research questions, the design principles of both observational and experimental methods, as well as the key principles and approaches to of variety of statistical techniques commonly used in health and social research and quality improvement practice.



Students will take a series of lectures and laboratory sessions to develop digital capabilities and employability skills such as choosing and implementing statistical methods on modern statistical software and reporting of statistical techniques and analysis results. Students will learn and practice handling real health data, and design, implement, and report their own statistical analyses as parts of the in-class workshops and module assessments.

Module provider

School of Health Sciences

Module Leader

HARRIS Jenny (Health Sci.)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None

Module content

The module will cover the following topics:

Types of study and variables

Statistical hypotheses and parametric and non-parametric tests

Multivariable analysis and regression modelling

Longitudinal data modelling and time series analysis

Survival analysis

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to enable students to demonstrate their ability to select, implement, report, and critically appraise statistical techniques as applied to healthcare and social research. Through both formative and summative assessments, students will evidence their understanding of key concepts, principles, and applications of statistical methods in these contexts. 

 The summative assessment for this module consists of: 

1. Recorded audio visual presentation (30%) 

Students will submit a 10-minute recorded audio-visual presentation of their statistical analysis plan and results. This presentation will critically appraise their analytical approach and aim to effectively communicate key results. (Addressing learning outcome 4-5)  

      2. Data Analysis Report (70%) 

Students will complete a data analysis report (2000 words) based on a supplied dataset. The submission will take the form of a detailed statistical analysis plan addressing a quantitative research question, results and summary discussion. Students will: select and justify appropriate statistical methods; implement the analysis correctly (providing evidence of the application of the analysis); accurately report and interpret results; and critically appraise any limitations of their chosen methods and analytical work. (Addressing learning outcomes 1-5)  

 Formative assessment 

Students can submit an outline of their analysis and their planned presentation for feedback using a proforma provided by the module team. Constructive feedback and guidance will be provided to ensure they are on the right track. 

 Feedback 

Students will receive written feedback from the module lead and team on all assessed elements, and there will be regular verbal feedback/discussions on the practical and guided learning tasks.

Module aims

• Introduce students to the key concepts and principles of statistics and quantitative methodology as applied to healthcare research.
• Introduce the variety of statistical techniques commonly used in health and social research and practice.
• Provide students with skills in handling real health data, including the design, critical appraisal, and implementation of statistical analyses and properly reporting analysis results.
• Equip students with knowledge and skills in R coding that will support further learning beyond the module (e.g. Machine Learning and AI).

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to demystify quantitative statistical research designs to ensure that students achieve the module¿s learning outcomes and develop competencies in the corresponding aspects of the curriculum framework (employability, digital capabilities, global and cultural intelligence, and resourcefulness and resilience). 

This module will be taught in a series of lectures and computer laboratory sessions. The lectures will introduce key concepts and principles of statistical methods commonly used in healthcare and biomedical research and practice, followed by tutorials on implementing the methods on statistical software (e.g., R) developing knowledge and appreciation through experiential learning techniques.   

As the module is likely to be predominately by health and social care professionals, also working in clinical practice, the learning and teaching strategy will draw on the importance of sharing ideas, peer support and interprofessional working. A workshop will be organised to provide students with the opportunity to teach-learn from each other and explore an interested topic with greater depth.  

The learning and teaching methods will therefore include: 

• Lectures 

• Computer laboratory sessions 

• Reading and self-learning under the module leader¿s guidance 

• Written and audio-visual assessments as described below

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: HCRM054

Other information

The School of Health Sciences is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, Resourcefulness and Resilience. This module is designed to develop knowledge, skills and capabilities in the following areas: Employability - Statistical knowledge and skills taught in this module will be of value when students¿ progress to their next stage of their careers in data sciences and informatics. Students will develop the capabilities in handling real health data using basic and advanced parametric and non-parametric statistical methods and learn and practice reporting and presentation skills, which may be particularly valued by prospective employers. Through lectures, laboratory sessions, and workshop, students will approach to various statistical resources that could be re-used in their future career. Digital Capabilities - Statistical software and digital resources will be fully integrated in this module. Students will have the chance to learn and develop their statistical programming capabilities not only in the laboratory sessions but also in the lectures through co-developing and testing statistical codes with the module leader and their peer students and in the workshop through teach-learning from peers. Global Capabilities, Resourcefulness and Resilience - Students will approach to global resources and online communities that are playing a key role in supporting modern statisticians. Especially, students will explore the vibrant R community that has developed and shared numerous statistical packages.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.