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PHPS40190

Academic Year 2026/2027

Biostatistics I (PHPS40190)

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:
On Campus
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, health, 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 biological or demographic 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 (biological) subjects. The student will also learn how to choose and fit simple statistical models to data collected on samples, on groups of subjects, 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, introducing the R statistical software environment and the RStudio IDE. Examples and exercises will also be provided in other software packages. This will provide hands-on training in handling data, transforming and deriving new variables, describing data including with visuals, and a range of statistical tests to detect associations between two measures.

About this Module

Learning Outcomes:

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

- Select and perform basic statistical analyses commonly required in bio/medical research, including descriptive statistics, chi square and t tests, ANOVA models, correlation and simple linear regression, and selected non-parametric tests.
- Write up the statistical methods and findings of simple statistical procedures clearly, and accordance with best practice.
- Correctly interpret and critique the results of simple statistical analyses of biological, medical or related data.

Note that at the module coordinators discretion a viva voce may be used as an additional oral assessment, for some students, to clarify understanding of the material and/or software skills.

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: understanding and using ANOVA
- Correlation
- Linear regression
- Analysis of survival data
- Non-parametric tests
- Error, bias and reliability
- Trends in the transparent use of statistics in research

UNESCO highlights a set of key competencies that support individuals in their development and support society in achieving the UN Sustainable Development Goals (SDGs). UCD has adapted these competencies and is combining them with others to form a general framework of learning competencies. This module will help you develop the competencies below to the levels specified. 
Learning Competency Additional Information Level

Critical Thinking

 The ability to question norms, practices and opinions; to reflect on one’s own values, perceptions and actions. Competent

Systems Thinking

 The ability to recognize and understand relationships; to analyse complex systems; to think of how systems are embedded within different domains and different scales; and to deal with uncertainty. Competent

Digital Literacy and Judgement

 The ability to access, evaluate, create and communicate information in digital environments; to engage critically, ethically and responsibly with digital technologies and digital information; to understand their opportunities, limitations, risks and impact on individual’s digital identities; and to exercise informed judgement in digital participation and decision-making. Competent

AI Literacy & Agency

 The ability to understand, critically evaluate and responsibly engage with artificial intelligence; to recognise how AI systems are shaped by human values; to assess their ethical, social and environmental implications; and to exercise human judgement, agency and accountability in AI-related contexts. Competent

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


Goal Name Description Coverage

End poverty in all its forms everywhere 1

End hunger, achieve food security and improved nutrition and promote sustainable agriculture 1

Ensure healthy lives and promote well-being for all at all ages 1

Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all 1

Achieve gender equality and empower all women and girls 1

Ensure availability and sustainable management of water and sanitation for all 1

Ensure access to affordable, reliable, sustainable and modern energy for all 1

Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all 1

Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation 1

Reduce inequality within and among countries 1

Make cities and human settlements inclusive, safe, resilient and sustainable 1

Ensure sustainable consumption and production patterns 1

Take urgent action to combat climate change and its impacts 1

Conserve and sustainably use the oceans, seas and marine resources for sustainable development 1

Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss 1

Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels 1

Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development 1
 

Student Effort Hours:
Student Effort Type Hours
Tutorial

6
Computer Aided Lab

24
Specified Learning Activities

24
Autonomous Student Learning

30
Online Learning

24
Total

108

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
Learning Requirements:


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): Mid-term MCQ examination, with computations and data interpretation Week 7 Standard conversion grade scale 40% No
40
 Yes
Exam (In-person): Statistical scenarios, decisions and computations. Interpretation of data in tables and graphs.
 End of trimester
Duration:
2 hr(s)
 Standard conversion grade scale 40% Yes
60
 Yes

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, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

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.

Essential Medical Statistics by Betty Kirkwood and Johnathan Sterne (2nd ed). Print ISBN: 9780865428713; eBook ISBN: 9781444392845. Both available in UCD Library: https://go.exlibris.link/1mxr1ZBz

Danielle Navarro, Learning Statistics with R (v0.6). eBook at https://learningstatisticswithr.com/

Danielle J. Navarro and David R. Foxcroft, Learning Statistics with jamovi: A Tutorial for Beginners in Statistical Analysis. Cambridge, UK: Open Book Publishers, 2025, https://doi.org/10.11647/OBP.0333

Nathaniel Phillips. YaRrr! The Piarte's Guide to R. 2026. https://nathanieldphillips-yarrr.share.connect.posit.cloud/

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 7, 8, 9, 10 Wed 14:00 - 15:50
Autumn Lecture Offering 1 Week(s) - 11 Wed 14:00 - 15:50
Autumn Lecture Offering 1 Week(s) - 12 Wed 14:00 - 15:50
Autumn Lecture Offering 1 Week(s) - 6 Wed 14:00 - 15:50
Autumn Tutorial Offering 1 Week(s) - Autumn: Even Weeks Fri 15:00 - 15:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Wed 15:00 - 16:50
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