Credit points


Campus offering

Find out more about study modes.

Unit offerings may be subject to minimum enrolment numbers.

Please select your preferred campus.

  • Term Mode
  • Semester 1Multi-mode
  • Term Mode
  • Semester 1Online Unscheduled




HLSC642 Biostatistics for Health Sciences , HLSC647 Quantitative Research Methods

Unit rationale, description and aim

Understanding, using and interpreting statistics is crucial to public health research and practice, particularly in monitoring health outcomes and decision-making processes about interventions. This unit will develop students' knowledge of fundamental statistical concepts, such as descriptive and inferential statistics, common statistical tests and statistical methods commonly used in public health. This will include hypothesis testing, estimation, associations, modelling relationships and prediction using different methods such as logistic regression. Throughout the unit, students will consolidate their understanding of statistical theory through its application to practice. While there are some formulae and computational elements to the unit, the emphasis is on interpretation and concepts. Besides the theoretical material, this unit will also enable students to run basic analyses using common statistical software. Using this software, students will analyse simulated public health datasets and then interpret the results obtained. Statistical understanding and analytical expertise developed by students during the unit will then be applied to practice through critical appraisal statistical methods used in public health research.

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

Learning Outcome NumberLearning Outcome DescriptionRelevant Graduate Capabilities
LO1Demonstrate specialised knowledge of statistical concepts and their application to public health practiceGC1, GC2
LO2Distinguish between different statistical tests, especially in terms of application and interpretationGC1, GC2, GC7, GC9
LO3Perform appropriate statistical analysis using common statistical software and interpret the resultsGC1, GC2, GC7, GC8, GC10, GC11
LO4Develop a sound statistical approach to the analysis and interpretation of public health data and communicate findings in an academic-standard outputGC1, GC2, GC7, GC8, GC9, GC10, GC11
LO5Critique public health research on the basis of its statistical methods, analysis and interpretationGC1, GC2, GC7, GC9, GC11


Topics will include: 

Fundamental statistical concepts and methods 

  • Types and levels of measurement of quantitative data and measures of central tendency and variability 
  • Probability distributions 
  • Hypothesis testing 
  • Statistical confidence: confidence intervals, p-values, statistical vs practical significance 
  • Common statistical tests: comparison of means (between two or more dependent or independent groups), proportions  
  • Variability and statistical inference; power and sample size; bias, confounding and adjustment  

Application to public health practice 

  • Key measures of association in public health: relative risk, attributable risk, odds ratios 
  • Inferential statistics: correlation, linear regression, logistic regression, analysis of variance 
  • Use of statistical software to analyse quantitative datasets: common statistical tests 
  • Writing up statistical analyses: interpretation, requirements for expressing statistical results  
  • Critical appraisal of statistical methods in public health research: common tools and approaches 

Learning and teaching strategy and rationale


In multi-mode, students acquire essential biostatistical knowledge via a series of weekly face-to-face tutorials, which are supplemented by asynchronous online lectures that include recorded lecture content; online readings, online videoconferences and self-directed learning activities. The unit uses an active learning approach to support students as they gain knowledge of biostatistics and the applications of biostatistics in public health practice. Activities focus on analysis of simulated public health datasets or other material to provide context to student analysis and interpretation and an authentic learning experience. Students are provided with the opportunity to attend facilitated tutorial classes so as to participate in the development and synthesis of this knowledge with other students and deepen their level of understanding and engage in peer learning while receiving expert support for skill development.

Learning activities are designed to be suitable for both multimode and online deliveries to ensure students are achieving learning outcomes equitably. Learning content will be adapted between modes of delivery to account for current information and communication technologies.

The learning and teaching strategies of this unit are designed to allow students to meet the aims, learning outcomes of the unit, and graduate attributes of the University. Students will be expected to take responsibility for their learning and to engage actively with unit content and learning activities.

Assessment strategy and rationale

A range of assessment procedures will be used to meet the unit learning outcomes and develop graduate attributes consistent with University assessment requirements. In order to successfully complete this unit, students need to complete and submit three graded assessment tasks and obtain an aggregate mark of greater than 50%.

PUBH620 involves assessment tasks designed to introduce students to the broad range of activity involved in biostatistics. In Assessment Task 1 , students are required to demonstrate their understanding by analysing a public health dataset. In Assessment Task 2, students are required to apply their biostatistical skills by preparing statistical methods and analysis for a peer reviewed journal article. Finally, in Assessment Task 3 students will critique and interpret the statistics in current published journal articles and reflect on how biostatistical knowledge will shape their practice in public health.

All assessment tasks will be submitted electronically.

Overview of assessments

Brief Description of Kind and Purpose of Assessment TasksWeightingLearning Outcomes

Assessment task 1: Written task

Analysis of simulated public health dataset. This will enable students to develop an understanding of data analysis by analysing a public

health dataset.


LO1, LO2, LO3, LO4

Assessment task 2: Preparation of statistical methods and analysis for a peer-reviewed journal article. This will enable students to deepen their knowledge of data analysis by writing the methods and data analysis sections of a journal article.


LO2, LO3, LO4, LO5

Assessment task 3: Biostatistics reflective practice and critique exercises. This will enable students to reflect upon their learning in the unit and critique how they relate to competency standards for public health practice.


LO1, LO5

Representative texts and references

Barton, B., & Peat, J. K. (2014). Medical statistics : a guide to SPSS, data analysis, and critical appraisal (2nd ed.). Wiley-Blackwell. 

Bush, H. M. (2012). Biostatistics: An applied introduction for the public health practitioner. Clifton Park, NY: Delmar Cengage Learning.  

Celentano, D. D., & Szklo, M. (2019). Gordis epidemiology (Sixth edition.). Elsevier.

Kirkwook, B.R., & Sterne, J.A.C. (2003). Essential medical statistics 2nd ed. Malden, Massachusetts: Blackwell Science [ACU ebook]

Moore, D. S., Notz, W., & Fligner, M. A. (2018). The basic practice of statistics (Eighth edition.). New York, NY: Macmillan Education.

Pallant, J. F. (2016). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (6th ed.). Crows Nest: Allen & Unwin. 

Portney, L. G. (2020). Foundations of clinical research: Applications to practice (4th ed.). F. A. Davis

Rowntree, D. (2018). Statistics Without Tears: An Introduction For Non-Mathematicians (1st ed.) Penguin Press.

Wagner, W. E., & Gillespie, B. J. (2019). Using and interpreting statistics in the social, behavioral, and health sciences. SAGE Publications, Inc.

Have a question?

We're available 9am–5pm AEDT,
Monday to Friday

If you’ve got a question, our AskACU team has you covered. You can search FAQs, text us, email, live chat, call – whatever works for you.

Live chat with us now

Chat to our team for real-time
answers to your questions.

Launch live chat

Visit our FAQs page

Find answers to some commonly
asked questions.

See our FAQs