Outline

Overview

A successful data analyst, scientist or data manager must have the ability to collect, analyse and interpret data in robust and meaningful ways. Central to this skillset is the ability to create hypotheses and test these using rigorous processes. This unit introduces the key concepts of study design and analysis. You will learn to formulate experimental aims, collect, interpret and analyse data to test specific hypotheses, and draw evidence-based conclusions. You will develop the skills and understanding of concepts such as controls, replicates, sample size, dependent and independent variables, and good research practice such as blinding and randomisation. You will emerge with a comprehensive understanding of how to optimise a product or web design and determining the effectiveness of new programs or solutions to understand if the observed changes in your data are systematic or if they may have happened by chance.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Fundamentals of statistics and coding in R, e.g. ODAT5011: Data Analytics Foundations
Available to study abroad and exchange students	No

Teaching staff

Coordinator	John Ormerod, john.ormerod@sydney.edu.au

Assessment

The census date for this unit availability is 14 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Practical test ?	Week 2 Review Questions Short weekly quiz on the lecture material.	3%	Week 03 Due date: 14 Mar 2025 at 23:59 Closing date: 21 Mar 2025	1 hour
Outcomes assessed:
Assignment	Unit Two Homework Problem Short R programming problem	5%	Week 03 Due date: 14 Mar 2025 at 23:59 Closing date: 21 Mar 2025	1 Week
Outcomes assessed:
Practical test ?	Week 3 Review Questions Short weekly quiz on the lecture material.	3%	Week 04 Due date: 21 Mar 2025 at 23:59 Closing date: 28 Mar 2025	1 hour
Outcomes assessed:
Assignment	Unit Three Homework Problem Short R programming problem	5%	Week 04 Due date: 21 Mar 2025 at 23:59 Closing date: 28 Mar 2025	1 Week
Outcomes assessed:
Practical test ?	Week 4 Review Questions Short weekly quiz on the lecture material.	3%	Week 05 Due date: 28 Mar 2025 at 23:59 Closing date: 04 Apr 2025	1 hour
Outcomes assessed:
Assignment	Unit Three Assignment One Experimental design assignment. Given several scenarios students design, at a reasonable level of detail, the decisions they would take to conduct an experiment and statistics to test a hypothesis.	30%	Week 05 Due date: 28 Mar 2025 at 23:59 Closing date: 04 Apr 2025	2-3 page report
Outcomes assessed:
Assignment	Unit Four Homework Problem Short R programming problem	5%	Week 05 Due date: 28 Mar 2025 at 23:59 Closing date: 04 Apr 2025	1 Week
Outcomes assessed:
Practical test ?	Week 5 Review Questions Short weekly quiz on the lecture material.	3%	Week 06 Due date: 04 Apr 2025 at 23:59 Closing date: 01 Apr 2025	1 hour
Outcomes assessed:
Assignment	Unit Five Homework Problem Short R programming problem	5%	Week 06 Due date: 04 Apr 2025 at 23:59 Closing date: 11 Apr 2025	1 Week
Outcomes assessed:
Practical test ?	Week 6 Review Questions Short weekly quiz on the lecture material.	3%	Week 07 Due date: 11 Apr 2025 at 23:59 Closing date: 13 Apr 2025	1 hour
Outcomes assessed:
Assignment	Unit Six Homework Problem Short R programming problem	5%	Week 07 Due date: 11 Apr 2025 at 23:59 Closing date: 13 Apr 2025	1 Week
Outcomes assessed:
Assignment	Unit Six Assignment Two Explore a given data story via a statistical analysis. Students will be asked to dig deeper by asking their own questions about the data and visualise their results.	30%	Week 08 Due date: 18 Apr 2025 at 23:59 Closing date: 25 Apr 2025	4-5 page report
Outcomes assessed:
= AI allowed ?

Assessment summary

Each week has homework revision questions and an exercise. There are two written assignments, the first on experimental design, and the second on a data analysis in R.

Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100	A high distinction indicates work of an exceptional standard
Distinction	75 - 84	A distinction indicates work of an very high standard
Credit	65 - 74	A credit indicates work of a good standard
Pass	50 - 64	A pass indicates work of an acceptable standard
Fail	0 - 49	The learning outcomes of the unit of study have not been met to a satisfactory standard.

For more information see guide to grades.

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

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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.

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	Course overview. Statistics refresher and R setup.	Workshop (1.5 hr)
Week 01	Statistics refresher and R setup.	Independent study (4 hr)
Week 02	Sampling, sampling variation, and stratified sampling.	Workshop (1.5 hr)
Week 02	Sampling, sampling variation, and stratified sampling.	Independent study (4 hr)
Week 03	Key elements of experimental design	Workshop (1.5 hr)
Week 03	Key elements of experimental design	Independent study (4 hr)
Week 04	Two sample t-tests and ANOVA	Workshop (1.5 hr)
Week 04	Two sample t-tests and ANOVA	Independent study (4 hr)
Week 05	Simple and multiple regression. Assumptions and interpretation.	Workshop (1.5 hr)
Week 05	Simple and multiple regression. Assumptions and interpretation.	Independent study (4 hr)
Week 06	Logistic (binary) and Poisson (count) regression. Course wrap-up.	Workshop (1.5 hr)
Week 06	Logistic (binary) and Poisson (count) regression.	Independent study (4 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. Describe and identify the basic features of an experimental design.
LO2. Analyse data using appropriate linear and generalised linear models in R.
LO3. Describe and visualise the features and interpretations of linear and generalised linear models.
LO4. Critique experimental designs and modelling choices in a range of scenarios.
LO5. Explain and justify statistical design considerations, modelling processes and conclusions effectively to a variety of audiences.
LO6. Plan, execute and analyse a study using real-world data.

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 is the first time this unit has been offered.

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

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

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

Disclaimer

On this page

Useful links

ODAT5021: Study Design and Analysis

PG Online Session 1A, 2025 [Online] - Online Program

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

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

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

Disclaimer

On this page

Useful links