Unit outline_

# FMHU5002: Introductory Biostatistics

## 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 6 None None PUBH5018 None Yes

### Teaching staff

Coordinator Erin Cvejic, erin.cvejic@sydney.edu.au Shazia Shehzad Abbas Erin Cvejic Armando Teixeira-Pinto

## Assessment

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.

### 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.

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 Learning outcomes
Week 01 jamovi self-directed learning task Independent study (2 hr)
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 1 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)
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.

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.

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

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

GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

## 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.