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 allow students to demonstrate their acquired knowledge and skills to properly choose, implement, report, and critically appraise statistical techniques as applied to healthcare and social research and practice. Through both the formative and summative assessment, students will demonstrate their understanding and exploration of key concepts, principles and applications of statistical techniques in healthcare and social research and practice.

Thus, the summative assessment for this module consists of:

Workshop presentation and participation in questions and discussions critically appraising their own proposed research and statistical analysis plan, 10 minutes of presentation followed by 5 minutes of questions and discussions. (Addressing learning outcome 4-5) (30%)

Assessment will be based on a 3000-word written assignment comprised of detailed research proposal and statistical analysis plan to answer a quantitative research question. Students will be required to choose and detail how they would implement appropriate statistical method(s) to achieve the aim(s)/goal(s), properly report their chosen method(s) and analysis results, and critically appraise the limitation of their method(s) and analytical work. (Addressing learning outcomes 1-5) (70%)

Formative assessment Students will submit an outline of their planned presentation for workshop; feedback and guidance will be provided to ensure students are on the right track.

Feedback Students will receive written feedback from the module lead and/or teaching assistant on each of the assessment elements; this includes the weekly coursework, workshop presentation slides, and the writing and statistical programming assessments. Verbal feedback will also be provided following the workshop presentation.

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:

• Equip the students with knowledge and to develop an enquiring mind in relation to theories of pain mechanisms and consideration of holistic factors.


• Enable students to develop a holistic approach to pain assessment and management as applied within their specific area of practice.


• Empower the students to have confidence that they can influence practice.

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.