Introduction to Biostatistics PG (12118.1)

Please note these are the 2025 details for this unit
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Introduction to Biostatistics PG
Available teaching periods Delivery mode Location
View teaching periods On-campus
Online real-time
 UC Sydney Hills, Castle Hill, NSW
Bruce, Canberra
EFTSL Credit points Faculty
0.125 3 Faculty Of Health
Discipline Study level HECS Bands
Public Health Post Graduate Level Band 2 2021 (Commenced After 1 Jan 2021)
Band 3 2021 (Commenced Before 1 Jan 2021)
The unit will provide students with a solid foundation in statistical methods in public health including exposure to and use of statistical software packages ( STATA, R or SPSS) required for population health research practice. The unit will build student skills and confidence to independently explore, analyse and interprete health data.

Learning outcomes

Upon successful completion of this unit, students will be able to:
1. Demonstrate an understanding of the importance of biostatistics in public health;
2. Understand and explain the concept of probability and sampling, formulate research hypotheses into a statistical context;
3. Conduct hypothesis tests for comparison of means, proportions, incidence rates and survival curves;
4. Identify data into appropriate measurement types and apply for visualization and summarization; and
5. Perform statistical analyses using STATA, R or SPSS.


Graduate attributes

1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
1. UC graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict
2. UC graduates are global citizens - adopt an informed and balanced approach across professional and international boundaries
3. UC graduates are lifelong learners - reflect on their own practice, updating and adapting their knowledge and skills for continual professional and academic development


Skills development

This course is intended to convey principles and methods of reasoning that underlie Biostatistics. It will provide knowledge and abilities regarding specific descriptive and inferential techniques commonly used in population health practice and research. 

 



Prerequisites

None.

Corequisites

None.

Incompatible units

None.

Equivalent units

None.

Assumed knowledge

None.
Year Location Teaching period Teaching start date Delivery mode Unit convener
2026 UC Sydney Hills, Castle Hill, NSW Semester 1 16 February 2026 On-campus Assoc Prof Theo Niyonsenga
2026 Bruce, Canberra Semester 1 16 February 2026 On-campus Assoc Prof Theo Niyonsenga
2026 Bruce, Canberra Semester 1 16 February 2026 Online real-time Assoc Prof Theo Niyonsenga
Not available

Required texts

There is no single book covering this course contents. The books listed below are useful for this course. I
would recommend Books 1, 2 & 5 since they help to discover Statistics theory using the different statistical
software packages (JAMOVI, SPSS, SAS & STATA). Books 3 & 4 cover the statistical theory with applications using
different data sets. Other relevant materials (such as data sets, articles and other pdf materials) will be posted
and uploaded on the course web site.


Navarro DJ and Foxcroft DR (2019). Learning statistics with JAMOVI: a tutorial for psychology students and other beginners. (Version 0.70). https://www.learnstatswithjamovi.com/

Field A. Discovering Statistics using IBM SPSS Statistics, 4rd edition. 2013 Sage Publications Ltd. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://sadbhavnapublications.org/research-enrichment-material/2-Statistical-Books/Discovering-Statistics-Using-IBM-SPSS-Statistics-4th-c2013-Andy-Field.pdf

Bernard Rosner. Fundamentals of Biostatistics, 8th Edition. Cengage Learning (edition 8). https://www.pdfdrive.com/fundamentals-of-biostatistics-8th-ed-d88350263.html

Kleinbaum DG, Kupper LL, Nizam A, and Rosenberd ES. Applied Regression Analysis and Other
Multivariable Methods, 5th edition. 2014 Cengage Learning. https://www.perlego.com/book/801958/applied-regression-analysis-and-other-multivariable-methods-pdf

Twisk, J. W. R. (2013). Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide: Cambridge University Press. https://library.canberra.edu.au/discovery/fulldisplay?context=L&vid=61ARL_CNB:61ARL_CNB&search_scope=MyInst_and_CI&tab=Everything&docid=alma991004896985703996

Juul S & Frydenberg M. An Introduction to Stata for Health Researchers, Fourth Edition. 2014 Stata Press.

Submission of assessment items

Extensions & Late submissions

Artificial intelligence

Students are not permitted to use generative artificial intelligence (GenAI) in assessments for this unit, except for Studiosity Writing Feedback Plus.

GenAI may only be used in authorised ways when completing assessments at UC. This means that GenAI can only be used for an assessment when:

  1. the Unit Convener has authorised GenAI use for that assessment
  2. the student uses GenAI in the way that the assessment instructions allow
  3. the student fully acknowledges their use of GenAI, with proper citations, references and a GenAI Acknowledgement Statement in line with the assessment instructions.

Where the assessment instructions do not specifically state that GenAI may be used and how, then its use is not permitted for that assessment. Students must still provide the required GenAI Acknowledgement Statement to indicate whether GenAI has or has not been used in the preparation of the assessment. If unsure, students should seek advice from the Unit Convener.

The GenAI for Students Library Guide provides further information, including how to reference GenAI Links to an external site..

 

Students must apply academic integrity in their learning and research activities at UC. This includes submitting authentic and original work for assessments and properly acknowledging any sources used.

Academic integrity involves the ethical, honest and responsible use, creation and sharing of information. It is critical to the quality of higher education. Our academic integrity values are honesty, trust, fairness, respect, responsibility and courage.

UC students have to complete the Academic Integrity Module annually to learn about academic integrity and to understand the consequences of academic integrity breaches (or academic misconduct).

UC uses various strategies and systems, including detection software, to identify potential breaches of academic integrity. Suspected breaches may be investigated, and action can be taken when misconduct is found to have occurred.

Information is provided in the Academic Integrity Policy, Academic Integrity Procedure, and University of Canberra (Student Conduct) Rules 2023. For further advice, visit Study Skills.

Learner engagement

Key to succeed in this unit: time commitment and consistency. 

Inclusion and engagement

Please let the unit convenor and teachers know as soon as possible is you will be needing an extension on any assignments and/or any help in your learning process. We will behappy to discuss the best ways to make "Biostatistics unit" successful for you.

Participation requirements

Attendance to tutorials are highly recommended. Students are expected to actively engage in this unit through Discussion pages, completing all online tasks and participating in ALL the Canvas discussion forums and activities. Assessment activities have been designed to stimulate students to put hands on the practical application and
how to do/perform methods they have been exposed to.

Required IT skills

  1. Information will be available to students via Canvas, an online educational hosting site.
  2. It is expected that students will have basic word processing skills and an ability to use databases to search for journal articles.
  3. Students will need access to statistical analysis software. SPSS or JAMOVI are encouraged and supported, but other options are available (e.g., R, STATA). Covidence and RevMan in systematic review & meta-analysis will also be involved in this course.
  4. Students must be able to use the statistical software they choose too and will get support. Examples and advice will generally be offered in SPSS or JAMOVI. This is not a software course, but candidates will gain statistical software proficiency throughout.
  5. Students are encouraged to use reference management software such as End Note.

Work placement, internships or practicums

None