Course Title: Biostatistics

Part A: Course Overview

Course Title: Biostatistics

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 1 2006


City Campus


145H Mathematical & Geospatial Sciences

Face-to-Face or Internet

Sem 1 2013,
Sem 2 2013


City Campus


145H Mathematical & Geospatial Sciences


Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 1 2011,
Sem 2 2011,
Sem 1 2012,
Sem 2 2012,
Sem 1 2014,
Sem 1 2015,
Sem 2 2015,
Sem 1 2016,
Sem 2 2016


City Campus


171H School of Science


Sem 1 2017,
Sem 2 2017

Course Coordinator: Dr Manjula Algama

Course Coordinator Phone: -

Course Coordinator Email:

Course Coordinator Location: Contact for appointment

Course Coordinator Availability: Contact for appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

A working knowledge of basic mathematics and medical terminology and familiarity with computers. Regular access to a computer with a high-speed internet connection on or off campus.

Course Description

This course will introduces you to the foundations of statistics as applied in biological, medical and epidemiological fields. The course will begin with an introduction to summary statistics, data visualisation and probability as a measure for uncertainty. The course will then build upon these topics by introducing statistical data investigations, sampling, sampling distributions and confidence intervals as the basis for statistical inference. The course will finish with a series of modules looking at common hypothesis testing methods for different types of data. The course emphasises conceptual understanding, interpretation of statistical output and the use of statistical software packages for statistical computation.

Objectives/Learning Outcomes/Capability Development

On completion of this course you should be able to:

1. Elucidate the concept of variation and identify and pose statistical questions requiring investigation in the biosciences and related fields.
2. Plan a statistical data investigation in the biosciences and related fields, including identifying variables and measures and proposing a method of data collection that will answer the question posed.
3. Collect, manage and store biostatistical data ready for analysis.
4. Apply fundamental biostatistical methods to explore, analyse and visualise data and test statistical hypotheses
5. Interpret biostatistical analysis and draw conclusions in context and in the presence of uncertainty
6. Use  statistical software packages for statistical computing and reproducible analysis.

This course contributes to the following Program Learning Outcomes for MC111 Master of Biotechnology and MC158 Master of Laboratory Medicine:

Personal and professional awareness

• the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
• the ability to reflect on experience and improve your own future practice
• the ability to apply the principles of lifelong learning to any new challenge.

Knowledge and technical competence

• an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.


• the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
• an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.


• the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

Information literacy

• the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.

Overview of Learning Activities

This course is delivered fully online through the learning management system (Blackboard). This will give you access to course information, communication tools, online notes and assessment activities. The delivery of this course is a little different to what you might expect in a normal lecture. Online course notes and video demonstrations replace lecture content. The regular exercise submissions, online tests and a major course project will help develop and assess your understanding. The course modules emphasise conceptual understanding and the use of technology for statistical computation. The online course notes feature detailed explanations of concepts, video demonstrations, and worked examples. This online material will make your individual study activity more flexible. Course communication will take place through an online community, where students can interact with the lecturer and their peers.


Ninety-six (96) hours for one semester. This will comprise two hours of self-study with online material and four hours of exercises, project work or revision spread across the 16 weeks of the semester (12 study weeks, 1 swotvac week and 3 exam weeks).

Overview of Learning Resources

There are no prescribed texts for this course. All course content, notes and learning materials will be available through the course notes website.
Students should have regular access to a computer with high-speed internet connection on or off campus.
Students will require access to Microsoft Excel (or equivalent) and a statistical software package. The course will support both a free, namely R, and commercial option through MyDesktop. Students will be notified in the course information of the commercial option and will also be given information to purchase a licence for personal use if required.

Overview of Assessment

☒This course has no hurdle requirements.

Assessment Tasks
Early Assessment Task:  Data Visualisation
An introductory data visualisation task and written summary due in the first quarter of the semester. 
Weighting 5%
This assessment task supports CLOs 3, 4, 5

Formative Assessment Task 2: Module Exercises 
Course module exercises that aim to develop student understanding and practice the application of statistics using technology.
Weighting 10%
This assessment task supports CLO 4, 5 and 6.

Assessment Task 3:  Project
A major course project comprising a proposal, data collection and poster summary.
Weighting 40%
This assessment task supports CLOs 1, 2, 3, 4, 5, and 6.

Assessment Task 4:  Online tests
One mid-semester and end of semester test during the exam period.
Weighting 45%
This assessment task supports CLOs 1, 4, 5 and 6.