FMHU5002: Semester 1, 2022

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Units FMHU5002 Semester 1 2022 [Online]

Unit of study_

FMHU5002: Introductory Biostatistics

Semester 1, 2022 [Online] - Camperdown/Darlington, Sydney

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

Unit code	FMHU5002
Academic unit	Public Health
Credit points	6
Prohibitions ?	PUBH5018
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Erin Cvejic, erin.cvejic@sydney.edu.au
Guest lecturer(s)	Shazia Shehzad Abbas, shazia.abbas@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	25%	Week 05	Available for two weeks, 1500 words
Outcomes assessed:
In-semester test (Record+)	Mid-semester online test Invigilated (Type B) online test - administered through Canvas	25%	Week 08 Due date: 14 Apr 2022 at 18:00 Closing date: 14 Apr 2022	1 hour
Outcomes assessed:
= Type B final exam ? = Type B in-semester exam ?

Assessment summary

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 will be provided 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 sydney.edu.au/students/guide-to-grades

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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
Week 01	jamovi self-directed learning task	Independent study (2 hr)
Week 01	Descriptive statistics	Lecture and tutorial (3 hr)
Week 02	Normal distribution, central limit theorem, confidence intervals for mean	Lecture and tutorial (3 hr)
Week 03	Hypothesis testing, p-values, one-sample t-tests	Lecture and tutorial (3 hr)
Week 04	Paired and independent t-tests	Lecture and tutorial (3 hr)
Week 05	Confidence intervals for proportions, hypothesis tests for one proportion and paired proportions	Lecture and tutorial (3 hr)
Week 06	Chi-squared test for independent proportions	Lecture and tutorial (3 hr)
Week 07	Small sample and non-parametric tests	Lecture and tutorial (3 hr)
Week 08	Power and Sample Size I	Lecture and tutorial (3 hr)
Week 09	Power and Sample Size II	Lecture and tutorial (3 hr)
Week 10	Correlation and simple linear regression	Lecture and tutorial (3 hr)
Week 11	Multivariable regression	Lecture and tutorial (3 hr)
Week 12	Statistics in Practice	Lecture (1 hr)
Week 13	Past Exam Tutorial	Tutorial (2 hr)
Week 13	Q+A Session	Forum (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.

Required readings

Recommended readings for this unit can be accessed through the the Library’s reading list system Leganto, available on Canvas

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 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 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, linear regression)
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.

This unit was previously branded as PUBH5018 Introductory Biostatistics. Several major changes have occurred over the past 2 years, including the introduction of a new statistical software program (jamovi) in 2020, and a substantial reduction in the focus on hand-calculation of numerical values and test statistics, instead encouraging the use of statistical software to perform these calculations (as is typically done in practice) with greater emphasis on the meaning, interpretation, and communication of these values in 2021. In response to student feedback in 2021, we have reduced the number of assessments by removing weekly assessed online quizzes - however these will still be made available as optional, non-assessed resources.

Additional information

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 2.2.5
Students will require access to a computing device (not a tablet or iPad) 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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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