Credit points


Campus offering

No unit offerings are currently available for this unit



Teaching organisation

150 hours of focused learning

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 - Explain 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 Validity
  • 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

ACU Online

This unit uses active learning, case-based learning, cooperative learning, web-based learning, and reflective/critical thinking activities. These strategies support students in their exploration of required knowledge as they develop an in-depth understanding of unit content. Opportunities are provided for application of knowledge and understanding for practical skill development in data analysis. Students are encouraged to contribute to asynchronous discussions. 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

ACU Online

In order to pass this unit, students are expected to submit two graded assessment tasks. In addition, students must achieve a cumulative grade of at least 50% across all assessments. In order to reward students for engagement and performance, a final graded result will be awarded. The assessment strategy used allows for the progressive development of knowledge and skills relevant to data analysis, interpretation and communication which are necessary for the student to be able to demonstrate achievement of learning outcomes.

To become effective at interpreting and communicating data analysis results, students must first develop comprehensive knowledge, skills and understanding of the statistical approaches used to analyse and interpret de-identified data collected within a High Performance Sport environment. A range of assessment strategies are used where data analysis tasks are purposefully designed to assess student learning of unit content and its application. The first assessment task requires students to use statistical approaches to analyse performance test data and interpret the results, being critical skills underpinning performance assessment and intervention. The final task a “Coach Report” has been designed to assess the student's underpinning knowledge and skills in analysing and interpreting data appropriately as well as the more complex requirement of communicating the outcomes clearly and effectively to in a method required within the High Performance Sport environment.

Overview of assessments

Brief Description of Kind and Purpose of Assessment TasksWeightingLearning OutcomesGraduate Attributes

Assessment 1

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

Assessment 2

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. 


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] 2016. 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

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