Credit points


Campus offering

No unit offerings are currently available for this unit



Unit rationale, description and aim

The ability to make sound decisions in high performance sport is critical to maximizing performance outcomes. In order to do this, practitioners need specific knowledge and skills in data analysis techniques, in addition to the ability to present data in a meaningful way to a variety of audiences. This unit is based on contemporary data analysis techniques focusing on determining practically meaningful differences in athletic performance. A range of approaches will be explored to allow for analysis of both individual and group data. The aim of this unit is to provide students with the knowledge, understanding and skills to analyse and interpret data of relevance to sports science and athletic performance and effectively present the results.

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.

On successful completion of this unit, students should be able to:

LO1 - Understand the importance of using appropriate statistical techniques for making effective decisions in high performance sport. (GA5, GA8)  

LO2 - Utilise contemporary statistical approaches to analyse individual and group data (GA5, GA8)  

LO3 - Interpret and report the outcome of statistical analyses in a way that effectively communicates complex information to a variety of audiences (GA8, GA10)  

Graduate attributes

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA8 - locate, organise, analyse, synthesise and evaluate information 

GA10 - utilise information and communication and other relevant technologies effectively.


Topics will include: 

  • Hypothesis Testing including p values and their limitations
  • Understanding and calculating measures of Reliability and validityValidity
  • Methods for determining practically important differences between groups
  • Methods for determining practically important differences in individuals
  • Advanced statistical approaches in High Performance Sport
  • Reporting and presenting outcomes to coaches and athletes 

Learning and teaching strategy and rationale

Learning and teaching strategies include active learning, case-based learning, cooperative learning, web-based learning, and reflective/critical thinking activities, delivered across 12 weeks. These strategies will provide students with access to required knowledge and understanding of unit content, and opportunities for application of knowledge and understanding for practical skill development in data analysis. These strategies will allow students to meet the aim, learning outcomes and graduate attributes of the unit. Learning and teaching strategies will reflect respect for the individual as an independent learner. Students will be expected to take responsibility for their learning and to participate actively in the online environment.  

Assessment strategy and rationale

In order to best enable students to achieve unit learning outcomes and develop graduate attributes, standards-based assessment is utilised, consistent with University assessment requirements. A range of assessment strategies are used including: a data analysis task to assess student learning of unit content and its application; and a “Coach Report” to assess the ability to interpret data appropriately and communicate the outcomes clearly and effectively.  

Overview of assessments

Brief Description of Kind and Purpose of Assessment TasksWeightingLearning OutcomesGraduate Attributes

Data analysis and interpretation task

Enables students to demonstrate their understanding of statistical approaches by analysing performance test data and interpreting the results. 


LO1, LO2 

GA5, GA8 

Data analysis interpretation and coach report  

Enables students to apply skills developed in the unit for the analysis of data and communication of the outcomes. 


LO1, LO2, LO3 

GA5, GA8, GA10 

Representative texts and references

Blume JD, D'Agostino McGowan L, Dupont WD, Greevy RA, Jr. (2018) Second-generation p-values: Improved rigor, reproducibility, & transparency in statistical analyses. PLoS ONE 13(3): e0188299.

Hopkins, W.G. A New View of Statistics. [Web page] 2000. Available from:

Quintana, D.S., Williams, D.R. Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP. BMC Psychiatry 18, 178 (2018).

Ronald L. Wasserstein & Nicole A. Lazar (2016): The ASA's statement on p-values: context, process, and purpose, The American Statistician, DOI: 10.1080/00031305.2016.1154108

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