Outline

Overview

This unit introduces students to statistical methods relevant in medicine and health. Students will learn how to build datasets and basic data management procedures, summarise and visualise data, choose the correct statistical analysis, conduct this analysis using statistical software, interpret its results, and report statistical findings in a format suitable for inclusion in scientific publications. Students will also learn to consider the difference between statistical significance and practical importance, and how to determine the appropriate sample size when planning a research study. Specific analysis methods covered in this unit include: descriptive methods; hypothesis tests for one sample, paired samples and two independent groups for continuous and categorical data; correlation and linear regression; power and sample size estimation for simple studies. All these topics are introduced with an emphasis on practical application and interpretation and are supported using statistical software. The general principles developed in this unit can be easily extended to more advanced methods; students who wish to continue with their statistical learning after this unit are encouraged to take PUBH5217 Biostatistics: Statistical Modelling.

Unit details and rules

Academic unit	Public Health
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	PUBH5018
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Andrew Grant, andrew.grant1@sydney.edu.au
Lecturer(s)	Farzaneh Boroumand, farzaneh.boroumand@sydney.edu.au
Lecturer(s)	Andrew Grant, andrew.grant1@sydney.edu.au
Tutor(s)	Trishala Sharma, trishala.sharma@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
Q&A following presentation, submission or placement	Analysis interpretation presentation Presentation followed by Q&A	30%	Formal exam period	3 min presentation plus Q&A	AI prohibited
Outcomes assessed:
Out-of-class quiz	Tutorial tasks Small tutorial tasks submitted online, distributed across semester	20%	Multiple weeks	4 x approx 200 words each	AI allowed
Outcomes assessed:
Written work	Data summary and analysis assignment Written assessment	20%	Week 04 Due date: 22 Mar 2026 at 23:59	1500 words	AI allowed
Outcomes assessed:
Written work	Data analysis and reporting assignment Written assessment	30%	Week 11 Due date: 17 May 2026 at 23:59	1500 words	AI allowed
Outcomes assessed:
= hurdle task ?

Assessment summary

Data summary and analysis assignment: Students will carry out, interpret and present descriptive statistical analyses for a provided dataset. Students will submit a written report that includes statistical software output.
Small continuous assessment: Students will complete four (4) small tutorial style tasks consisting of multiple choice, numerical, and short answer questions. Students may be required to use statistical software for these tasks. Submissions will be made online.
Data analysis and reporting assignment: Students will carry out and interpret a statistical analysis for a provided dataset. Students will submit a written report that includes statistical software output.
Analysis interpretation presentation: Students will prepare a short presentation which interprets and evaluates statistical analysis output. This presentation will be delivered live and followed by brief Q&A. Students will be required to use statistical software for this task.

All assessment items are hurdle tasks. Students must attempt all assessments in order to pass the unit. Detailed information for each assessment will be provided on Canvas.

It may be helpful to use artificial intelligence (AI) tools to suggest readability improvements to your text in terms of grammar and expression. However, you must ensure any assessment submission is your own, original work, and that the ideas presented are your own. You remain responsible for your work, so you must independently verify and edit AI-generated content to ensure the integrity, accuracy, and suitability of the output. Any use of generative AI must be appropriately acknowledged in submission, including describing the AI tool(s) used, what you used it to do, the prompts(s) you provided, and how any output was used and/or adapted by you. Failure to declare the use of AI tools is considered a breach of the Academic Integrity Policy and may result in penalties.

Assessment criteria

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

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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.

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	jamovi self-directed learning task	Self-directed learning (2 hr)
Week 01	Screening, summarising, and visualising data	Lecture (1 hr)
Week 02	Normal distribution, central limit theorem, confidence intervals for means	Lecture (1 hr)
Week 02	Screening, summarising, and visualising data	Tutorial (2 hr)
Week 03	Hypothesis testing, p-values, one-sample t-tests	Lecture (1 hr)
Week 03	Normal distribution, central limit theorem, confidence intervals for means	Tutorial (2 hr)
Week 04	Paired and independent t-tests	Lecture (1 hr)
Week 04	Hypothesis testing, p-values, one-sample t-tests	Tutorial (2 hr)
Week 05	Confidence intervals for proportions, hypothesis tests for one proportion and paired proportions	Lecture (1 hr)
Week 05	Paired and independent t-tests	Tutorial (2 hr)
Week 06	Chi-squared test for independent proportions	Lecture (1 hr)
Week 06	Confidence intervals for proportions, hypothesis tests for one proportion and paired proportions	Tutorial (2 hr)
Week 07	Small sample and non-parametric tests	Lecture (1 hr)
Week 07	Chi-squared test for independent proportions	Tutorial (2 hr)
Week 08	Power and sample size	Lecture (1 hr)
Week 08	Small sample and non-parametric tests	Tutorial (2 hr)
Week 09	Correlation and simple linear regression	Lecture (1 hr)
Week 09	Power and sample size	Tutorial (2 hr)
Week 10	Multivariable regression	Lecture (1 hr)
Week 10	Correlation and simple linear regression	Tutorial (2 hr)
Week 11	Multicategory explanatory variables	Lecture (1 hr)
Week 11	Multivariable regression	Tutorial (2 hr)
Week 12	Causality and DAGs	Lecture (1 hr)
Week 12	Multicategory explanatory variables	Tutorial (2 hr)
Week 13	Introduction to Data Analysis in R / Q+A Session	Lecture (1 hr)
Week 13	Causality and DAGs	Tutorial (2 hr)

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. choose appropriate measures to summarise data using numbers, tables, and graphs
LO2. visualise data using graphs and simple tables following recommendations for clear presentation
LO3. determine and calculate the appropriate summary statistics to describe different types of distributions
LO4. understand and explain the concepts of sampling and sampling distributions
LO5. choose the appropriate hypothesis test to apply based on the type of data collected and the design of the study
LO6. calculate and interpret confidence intervals for various measures of effect
LO7. conduct and interpret simple hypothesis tests for single, independent, and paired samples
LO8. understand and explain the difference between statistical significance and practical importance
LO9. carry out simple statistical methods using statistical software
LO10. interpret the output produced by statistical software
LO11. concisely summarise and communicate the results from a statistical analysis
LO12. determine and justify the sample size requirements for simple study designs

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.

In 2025, we trialled a new "partially-flipped" lecture format and made modifications to the way tutorials are delivered to increase engagement and interactivity. Student feedback was positive on these changes, and we will continue with this style of delivery in 2026

Additional information

ALL enquiries for this unit of study should be directed to the shared unit of study email address: fmhu5002@sydney.edu.au
Emails sent directly to unit coordinators personal accounts or via the Canvas mail system will not be responded to.
This unit uses the statistical software package jamovi, which can be downloaded (for free) from https://www.jamovi.org/ and installed on students’ own devices. The current version at time of publishing is Version 2.6.45.

jamovi

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

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

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

FMHU5002: Introductory Biostatistics

Semester 1, 2026 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

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

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