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PHPS41150

Academic Year 2024/2025

Introduction to Biostatistics (PHPS41150)

Subject:
Public Health & Population Sci
College:
Health & Agricultural Sciences
School:
Public Hlth, Phys & Sports Sci
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Ricardo Piper Segurado
Trimester:
Autumn
Mode of Delivery:
Online
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

A module covering essential and fundamental principles of statistics as applied in biology, medicine, and related fields.

The use of data to describe and infer properties of biological, clinical, and other human characteristics is increasing in academic research, and in many professional settings. It is important for the integrity of any conclusions drawn that such data analysis be conducted correctly, interpreted appropriately, and that transparency of methods and openness to assessment and critique be embedded in quantitative research.

This module will introduce the student to the quantification of human characteristics, and how to best describe them using numerical summaries, and visually. We will also provide an overview of the principles of statistical inference - drawing conclusions from data and understanding the uncertainty inherent in samples of human participants. The student will also learn how to choose and use simple statistical models of data collected on samples, on groups of participants, and on serial measurements over time.

The practical use of appropriate statistical software will be taught in parallel to the more theoretical aspects of the material.

About this Module

Learning Outcomes:

Upon completion of this module, students should be able to:

Understand and choose basic statistical analyses, including descriptive statistics, chi square and t tests, ANOVA models, correlation and simple linear regression, and selected non-parametric tests.

How to report methods and findings in accordance with best practice.

How to correctly interpret and critique the results of numerical analyses of biological, medical or related data.

Indicative Module Content:

- Data and descriptive statistics
- The Normal and other distributions
- Comparing means and proportions between groups: t tests and chi-square tests
- Comparing many means: between-subjects ANOVA, within-subjects ANOVA, mixed ANOVA
- Correlation
- Linear regression
- Non-parametric tests
- Error, bias and reliability
- Trends in the transparent use of statistics in research

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

30
Autonomous Student Learning

54
Tutorial

12
Computer Aided Lab

12
Online Learning

12
Total

120

Approaches to Teaching and Learning:
This module adopts a flexible, modular and problem-based approach to learning statistics.

Each week's session begins with a lecture covering the rationale, theory and mechanism for a particular statistical method or approach. Each lecture is accompanied by interactive sessions, with questions encouraged from the class, and with clear step-by-step examples where there is any mathematics involved.

Further weekly learning continues with practical computer exercises, familiarising the student with a software package, how to interpret the output, and report it correctly. Further exercise sheets for practice are provided.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Computer Lab assessment, consisting of a timed 1 hour set of exercises. Week 10 Standard conversion grade scale 40% No

40

 No
Assignment(Including Essay): Take-home assignment consisting of critique and interpretation of statistical results. Week 12 Standard conversion grade scale 40% No

60

 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Feedback on weekly formative (not graded) exercises will be given on request or automatically by the VLE, in advance of the Computer Lab assessment. Feedback on the computer lab assessment will be given individually to students through the VLE. General feedback will be given to the full class through the VLE.

Name Role
Ms Carolyn Ingram Lecturer / Co-Lecturer
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