Outline

Overview

This unit introduces fundamental statistical concepts and their application in healthcare. By engaging with real-world case studies, students will learn to analyse simple public health datasets gaining proficiency in software tools such as Excel and R, crucial for managing and analysing biological and health data. They will learn about the importance of clinical trials in evidence-based health care and explore ethical considerations, trial designs, and statistical principles that govern the evaluation of clinical research. This foundational unit serves as a critical stepping stone for students pursuing advanced studies and careers in allied health, medicine, public health, and biomedical research, providing them with the analytical toolkit necessary to thrive in these dynamic fields. By bridging theory with practice, students will gain not only knowledge but also the confidence to navigate and contribute to the evolving landscape of health sciences.

Unit details and rules

Academic unit	Department of Medical Sciences
Credit points	6
Prerequisites ?	None
Corequisites ?	BBHE1004
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Peter Mc Donald, peter.mcdonald1@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
In-class quiz	Early feedback task Workshop quiz with individual feedback on results	0%	Week 03 Due date: 10 Mar 2026 at 15:00 Closing date: 20 Mar 2026	10 minutes	AI allowed
Outcomes assessed:
In-class quiz	Workshop assignment Students will organise, clean and prepare a health-related data set, then conduct and interpret a simple data analysis. Students will receive individual feedback on results.	0%	Week 07 Due date: 14 Apr 2026 at 15:00 Closing date: 24 Apr 2026	60 minutes	AI allowed
Outcomes assessed:
Written work	Determinants of health poster Group scientific poster exploring the determinants of a health issue. This assessment will include an integrated data analysis task for companion unit BBHE1004.	0%	Week 12 Due date: 20 May 2026 at 23:59 Closing date: 30 May 2026	Scientific poster preparation	AI allowed
Outcomes assessed:
Interactive oral	Q&A following poster submission Live questioning (individually) regarding poster submission.	0%	Week 13 Due date: 26 May 2026 at 15:00 Closing date: 05 Jun 2026	15 minutes	AI prohibited
Outcomes assessed:
= group assignment ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Assessments in this unit are designed to evaluate competency and provide valuable feedback that supports the development of data literacy and practical skills essential for biomedical science.

The early feedback task will require students to complete an in-class quiz consisting of multiple-choice and short-answer questions. Answers will be discussed in class, with individual feedback provided to support and assess early learning. This is a formative task, and does not contribute toward the overall unit grade.
The workshop assignment is an in-class assessment requiring students to clean, prepare, and analyse a provided data set, and interpret the results using appropriate analytical methods. This is a summative assessment, and contributes to the final unit grade.
The determinants of health poster is a summative assessment integrated with BBHE1004 (Foundations in Public and Population Health). Working in groups, students will demonstrate data analysis and visualisation skills to address a key question related to a public or population health issue. This is a summative assessment and contributes to the final unit grade.
The Q&A following poster submission is an individual oral Q&A assessment based on the determinants of health poster submission, designed to assess students' understanding of their analysis, interpretation of results, and ability to communicate scientific evidence. This is a summative assessment and contributes to the final unit grade.

The summative assessments in this unit are: i) the workshop assignment, ii) the determinants of health poster, and iii) the Q&A following poster submission. All summative assessments in this unit are pass/fail. To pass this unit, students are required to fail no more than one summative assessment. Students who fail any summative assessment will be given one opportunity for remediation and re-assessment for that specific assessment.

Detailed instructions for each assessment can be found in Canvas.

Assessment criteria

Assessment grading (Grade-only unit):  

The University awards common result grades, set out in the Coursework Policy 2021 (Schedule 1).

Result name	Mark range	Description
Satisfied requirements	50 - 100	The learning outcomes of the unit of study have been met to a satisfactory standard.
Failed requirements	0 - 49	The learning outcomes of the unit of study have not been met to a satisfactory standard.

Paired assessment(s):

This unit contains the following paired assessment (s):

BBHE1004/1006 - Determinants of health poster (week 12)

BBHE1004/1006 - Q&A following submission of poster (week 13)

Marking of paired assessments will be as follows: 

Assessment of learning outcomes from one unit will be awarded only to that unit using an appropriate rubric. 

Assessment of learning outcomes in both units will be awarded to both units using an appropriate rubric.

Assessment of learning outcomes in grade-only units (i.e. BBHE1006), will be assessed against meeting the required competency level using an appropriate rubric.   

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late penalties (grade-only unit): On-time submission of assessments and meeting deadlines is a key aspect of professionalism in the healthcare sector. Assessments submitted late without permission (see Special Considerations: http://sydney.edu.au/students/special-consideration-and-arrangements.html) will incur a late penalty applied to the marking of your assessment. The late penalty will be applied within the ‘professionalism’ category of the rubric, where professionalism is graded ‘highly satisfactory’, ‘satisfactory’, ‘poor’ or ‘unsatisfactory’, or ‘automatic fail’. Assessments submitted 0-5 calendar days late will automatically incur a ‘poor’ grade for professionalism, and assessments submitted 6-10 calendar days late will incur an ‘unsatisfactory’ grade. Performance within the other assessment categories will determine if the late penalties for 0-5 and 6-10 days late result in an overall fail grade for the assessment. If the assessment is more than 10 calendar days late, it will incur the ‘automatic fail’ grade for professionalism, and the overall assessment will be failed.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to BBHE1006, and data and people	Self-directed learning (1 hr)
	Introduction to BBHE1006, and data and people	Tutorial (1 hr)
	Data and people	Workshop (2 hr)
Week 02	Data storage and organisation	Self-directed learning (1 hr)
	Data storage and organisation	Tutorial (1 hr)
	Data storage and organisation	Workshop (2 hr)
Week 03	Data quality and errors	Self-directed learning (1 hr)
	Data quality and errors	Tutorial (1 hr)
	Data quality and errors	Workshop (2 hr)
Week 04	Developing research questions and formulating hypotheses	Self-directed learning (1 hr)
	Developing research questions and formulating hypotheses	Tutorial (1 hr)
	Developing research questions and formulating hypotheses	Workshop (2 hr)
Week 05	Testing hypotheses	Self-directed learning (1 hr)
	Testing hypotheses	Tutorial (1 hr)
	Testing hypotheses	Workshop (2 hr)
Week 06	Effect vs association	Self-directed learning (1 hr)
	Effect vs association	Tutorial (1 hr)
	Effect vs association	Workshop (2 hr)
Week 07	Introduction to epidemiology	Self-directed learning (1 hr)
	Epidemiology	Tutorial (1 hr)
	Epidemiology	Workshop (2 hr)
Week 08	Measuring and interpreting risk in health data	Self-directed learning (1 hr)
	Measuring and interpreting risk in health data	Tutorial (1 hr)
	Measuring and interpreting risk in health data	Workshop (2 hr)
Week 09	Using qualitative data	Self-directed learning (1 hr)
	Using qualitative data	Tutorial (1 hr)
	Using qualitative data	Workshop (2 hr)
Week 10	Data visualisation using R	Self-directed learning (1 hr)
	Data visualisation using R	Tutorial (1 hr)
	Data visualisation using R	Workshop (2 hr)
Week 11	Science communication: Part 1	Self-directed learning (1 hr)
	Science communication: Part 1	Tutorial (1 hr)
	Poster workshop	Workshop (2 hr)
Week 12	Science communication: Part 2	Self-directed learning (1 hr)
	Science communication: Part 2	Tutorial (1 hr)
	Science communication: Part 2	Workshop (2 hr)

Attendance and class requirements

Participation: Each week this unit consists of two face-to-face classes, a 1hr tutorial and a 2 hr workshop/practical.

Participation in these face-to-face classes is compulsory, and a requirement to successfully complete the UoS. Students are expected to attend a minimum of 80% of timetabled activities as defined in the unit of study outline.

Before attending the tutorial and workshop/practical class, students should watch the accompanying lecturettes and complete any accompanying formative quizzes.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Understand foundational concepts of data collection and organisation, and presentation in the context of health and biomedical sciences
LO2. Perform simple data analysis from clinical studies, laboratory experiments, and public health research
LO3. Begin to work independently and collaboratively to interpret the results of data analyses such as research reports, health assessments, and clinical trials
LO4. Practice presenting and communicating research findings in a biomedical and health context

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This is the first time this unit has been offered.

Additional information

Passing the unit (Grade-only unit):

To pass this unit of study, students must: 

Fail no more than one of the summative assessments. 

Remediation and reassessment (Grade-only unit):

Students who fail a hurdle task, or their second non-hurdle summative assessment, will be given one opportunity for remediation and reassessment for that specific task. 

This includes receiving detailed feedback on the original failed assessment and additional teaching support comprising either: (i) an extra 1 hr tutorial or (ii) a 15-minute meeting with a member of academic staff. This opportunity will be provided within 10 days of the outcome of the original assessment being released, and whilst it will assess the same learning objectives, it could take a different format.

The one remediation and reassessment opportunity is in addition to any reassessment due to a successful application for special consideration. If a student is unable to attend the scheduled remediation reassessment opportunity, they may apply for special considerations as per the University of Sydney Assessment Procedures 2024.

Formative assessment:

Definition: Assessments that do not contribute to passing the unit or the final grade/mark

Purpose: To guide instruction, provide timely feedback, and help students improve.

Timing: Ongoing, frequent, during the learning process.

Stakes: Low-stakes or ungraded.

Feedback: Detailed, developmental, and immediate.

Examples: weekly practice quizzes, in-class questions and worksheets.

Summative assessment:

Definition: Assessments that do contribute to passing the unit or the final grade/mark

Purpose: To evaluate overall learning, measure achievement, and determine competency.

Timing: Mid-semester test (e.g. week 7/8), towards the end of the unit, exam period.

Stakes: High-stakes, graded, affects advancement.

Feedback: Final, evaluative (e.g., score, grade).

Examples: In semester tests, interactive oral exam, written exam.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

BBHE1006: Health Data: From Collection to Communication

Semester 1, 2026 [Normal day] - Westmead, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links