Outline

Overview

This unit introduces students to statistical methods relevant in medicine and health. Students will learn how to appropriately summarise and visualise data, carry out a statistical analysis, interpret p-values and confidence intervals, and present statistical findings in a scientific publication. Students will also learn how to determine the appropriate sample size when planning a research study. Students will learn how to conduct analyses using calculators and statistical software. Specific analysis methods of this unit include: hypothesis tests for one-sample, two paired samples and two independent samples for continuous and binary data; distribution-free methods for two paired samples, two independent samples; correlation and simple linear regression; power and sample size estimation for simple studies; and introduction to multivariable regression models;. 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 ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Erin Cvejic, erin.cvejic@sydney.edu.au
Lecturer(s)	Armando Teixeira-Pinto, armando.teixeira-pinto@sydney.edu.au
Lecturer(s)	Erin Cvejic, erin.cvejic@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam Invigilated (Type B) online exam - administered through CANVAS	50%	Formal exam period	1.5 hours
Outcomes assessed:
Assignment	Data summary and analysis Written assessment	20%	Week 05 Due date: 01 Apr 2021 at 23:59	Two weeks
Outcomes assessed:
In-semester test (Record+)	Mid-semester online test Invigilated (Type B) online test - administered through CANVAS	20%	Week 08 Due date: 30 Apr 2021 at 15:30	1 hour
Outcomes assessed:
Online task	Weekly quizzes Online quizzes	10%	Weekly	Each quiz is open for two weeks
Outcomes assessed:
= Type B final exam ? = Type B in-semester exam ?

Assessment summary

Weekly quizes: There will be 12 weekly online quizzes using a range of question formats. Each quiz is worth 1% and your 10 (of the 12) best quiz marks will contribute to your mark (i.e maximum mark of 10%). You have unlimited attempts for each quiz with your mark based on your most recent quiz attempt. Quiz content is accumulative, for example, Week 10 quiz can contain questions regarding Week 1 content. Conversely, a Week 9 quiz won’t necessarily mean that it will test Week 9 content (it may be tested later in another quiz).
Assignment: In this 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.
Mid-semester online test: The mid-semester test is open book and will consist of multiple choice, numerical, and short answer questions. Students will be required to use statistical software during this test. Further details of the timetable and registration will be released via Canvas at the start of semester.
Final exam: The final exam is open book and will consist of multiple choice, numerical, and short answer questions. Students will be required to use statistical software during this exam.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (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.

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.

Weekly schedule

WK	Topic	Learning activity
Multiple weeks	See the lecture and tutorial schedule of the study guide for weekly topics. Weekly from week 2 onward.	Tutorial (2 hr)
Multiple weeks	Question and Answer session. See the study guide for the weeks this is held in.	Lecture (1 hr)
Week 01	Self-directed jamovi learning task. Some guided sessions will be made available (see study guide)	Independent study (2 hr)
Weekly	See the lecture and tutorial schedule of the study guide for weekly topics	Lecture (1 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 tables and graphs
LO2. create simple tables and graphs following guidelines for clear presentation
LO3. determine and calculate the appropriate summary statistics to summarise 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 effects (e.g. single mean, difference in means, difference in proportions)
LO7. conduct and interpret simple hypothesis tests (e.g. one sample t-test, two-sample t-test, Chi-squared test)
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. write concise descriptions that summarise the results from a statistical analysis
LO12. determine the sample size requirements for simple studies.

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.

Several major changes were made to PUBH5018 in 2020, including the introduction of a new statistical software program - jamovi, the use of normative criteria-based marking for the written assignment, and the use of an online computer-based final exam. In 2021, we plan to further reduce the focus placed on the hand calculation of numerical values and test statistics in the notes, lectures, tutorials, and assessments, and instead encourage the use of statistical software to perform these calculations (as is typically done in practice). There will also be greater emphasis placed on the meaning, interpretation, and communication of these values. Assignment 1 will be due in week 5 instead of week 6. This will allow students to receive feedback prior to taking the mid-semester online test. Similarly, the mid-semester online test will be held slightly earlier (Week 8 instead of Week 9 / 10) than in previous years which will also allow students more time to digest their results and identify areas for improvement when preparing for the final exam. In response to student feedback in previous deliveries, the lecture and tutorial content on generating and presenting descriptive statistics for categorical and quantitative variables will be condensed from two modules down to one, whilst content on statistical power and sample size will be expanded from a single module to two modules.

Additional information

This unit will use the statistical software package jamovi, which can be downloaded (for free) from https://www.jamovi.org/ and installed on students’ own devices.
Students will require access to a computing device with a functioning webcam and jamovi installed to complete the mid-semester online test and final online exam.

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

PUBH5018: Introductory Biostatistics

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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