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Unit outline_

FMHU5002: Introductory Biostatistics

Semester 1, 2024 [Normal evening] - Camperdown/Darlington, Sydney

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 Erin Cvejic, erin.cvejic@sydney.edu.au
Lecturer(s) Andrew Grant, andrew.grant1@sydney.edu.au
Erin Cvejic, erin.cvejic@sydney.edu.au
Tutor(s) Lucy Corbett, lucy.corbett@sydney.edu.au
Kate Milledge, kate.milledge@sydney.edu.au
The census date for this unit availability is 2 April 2024
Type Description Weight Due Length
Assignment hurdle task Data analysis and reporting assignment
Written assessment
25% Formal exam period
Due date: 03 Jun 2024 at 23:59
1500 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11 LO12
Small continuous assessment hurdle task Tutorial tasks
Small tutorial tasks submitted online, distributed across semester
20% Multiple weeks 4 x approx 500 words each
Outcomes assessed: LO3 LO11 LO10 LO9 LO8 LO7 LO6 LO5 LO4
Assignment hurdle task Data summary and analysis assignment
Written assessment
30% Week 04
Due date: 17 Mar 2024 at 23:59
1500 words
Outcomes assessed: LO1 LO11 LO10 LO9 LO4 LO3 LO2
Presentation hurdle task Analysis interpretation presentation
Recorded presentation
25% Week 11
Due date: 12 May 2024 at 23:59
3 min presentation plus 250 word report
Outcomes assessed: LO1 LO11 LO10 LO9 LO8 LO7 LO6 LO5
hurdle task = hurdle task ?

Assessment summary

  • Data summary and analysis assignment: Students will carry out, interpret ands 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.
  • Analysis interpretation presentation: Students will prepare and record a short audiovisual presentation (3 minutes) for submission which interprets and evaluates statistical analysis output. This will be accompanied by a brief written report. Students will be required to use statistical software for this task. 
  • 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.

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.

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.

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 Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 2023 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 2023. 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

WK Topic Learning activity Learning outcomes
Week 01 jamovi self-directed learning task Independent study (2 hr) LO2 LO3 LO9 LO10
Screening, summarising, and visualising data Lecture and tutorial (3 hr) LO1 LO2 LO3 LO9 LO10 LO11
Week 02 Normal distribution, central limit theorem, confidence intervals for means Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO9 LO10
Week 03 Hypothesis testing, p-values, one-sample t-tests Lecture and tutorial (3 hr) LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11
Week 04 Paired and independent t-tests Lecture and tutorial (3 hr) LO5 LO6 LO7 LO8 LO9 LO10 LO11
Week 05 Confidence intervals for proportions, hypothesis tests for one proportion and paired proportions Lecture and tutorial (3 hr) LO5 LO6 LO7 LO8 LO9 LO10 LO11
Week 06 Chi-squared test for independent proportions Lecture and tutorial (3 hr) LO5 LO6 LO7 LO8 LO9 LO10 LO11
Week 07 Small sample and non-parametric tests Lecture and tutorial (3 hr) LO3 LO5 LO6 LO7 LO8 LO9 LO10 LO11
Week 08 Power and sample size Lecture and tutorial (3 hr) LO9 LO10 LO11 LO12
Week 09 Correlation and simple linear regression Lecture and tutorial (3 hr) LO2 LO3 LO5 LO6 LO7 LO8 LO9 LO10 LO11
Week 10 Multivariable regression Lecture and tutorial (3 hr) LO5 LO6 LO7 LO8 LO9 LO10 LO11
Week 11 Multicategory explanatory variables Lecture and tutorial (3 hr) LO5 LO6 LO7 LO9 LO10 LO11
Week 12 Causality and DAGs Lecture and tutorial (3 hr) LO2 LO5 LO7 LO9 LO10 LO11
Week 13 Introduction to Data Analysis in R / Q+A Session Lecture (1 hr) LO9 LO10 LO11

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 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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

This unit was branded as PUBH5018 Introductory Biostatistics up until 2021. In response to student feedback, we have modified the assessments by replacing the mid-semester in-class online test with a series of small in-class assessments dispersed across the semester (to be submitted online, which provides more flexibility in the timing of when the tasks can be completed, reducing time pressures for completion, and allowing for more regular feedback on progress). We have increased the marks allocated of the recorded presentation assessment commensurate with the amount of work required. We have also re-weighted the remaining assessments to be distributed more evenly across semester. We believe these assessment changes represent a more authentic form of assessment for the learning outcomes and content delivered within the unit. In 2024, we are introducing a new lecture topic in Week 11 on multiple category explanatory variables, and will also provide a gentle introduction to statistical analysis using R in Week 13 (there will also be an optional self-directed learning task to accompany this material). We hope that these additions will better prepare students for subsequent biostatistics coursework units and their future careers in health and medicine.
  • General administrative and support enquiries for this unit of study should be directed in the first instance to the Associate Lecturers in Epidemiology and Biostatistics, via email:
    sph.epibio@sydney.edu.au 
     
  • 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 2.3.28.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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