Outline

Overview

An indispensable attribute of an effective scientific researcher is the ability to collect, analyse and interpret data. Central to this process is the ability to create hypotheses and test these by using rigorous experimental designs. This modular unit of study will introduce the key concepts of experimental design and data analysis. Specifically, you will learn to formulate experimental aims to test a specific hypothesis. You will develop the skills and understanding required to design a rigorous scientific experiment, including an understanding of concepts such as controls, replicates, sample size, dependent and independent variables and good research practice (e.g. blinding, randomisation). By completing this unit you will develop the knowledge and skills required to appropriately analyse and interpret data in order to draw conclusions in the context of an advanced research project. From this unit of study, you will emerge with a comprehensive understanding of how to optimise the design and analysis of an experiment to most effectively answer scientific questions.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	144 credit points of units of study and including a minimum of 24 credit points at the 3000 or 4000-level and 18 credit points of 3000 or 4000-level units from Science Table A or FMH Table A
Corequisites ?	None
Prohibitions ?	ENVX3002 or STAT3X22 or STAT4022 or STAT3X12
Assumed knowledge ?	Completion of units in quantitative research methods, mathematics or statistical analysis at least at 1000-level
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Garth Tarr, garth.tarr@sydney.edu.au
Lecturer(s)	Garth Tarr, garth.tarr@sydney.edu.au
Tutor(s)	Rajan Shankar, rajan.shankar@sydney.edu.au

Assessment

The census date for this unit availability is 14 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Tutorial quiz	Quiz 1 Written in class quiz	15%	Week 04 Due date: 18 Mar 2025 at 12:00	90 minutes
Outcomes assessed:
Assignment	Statistical design critique Written task	20%	Week 05 Due date: 28 Mar 2025 at 23:59 Closing date: 07 Apr 2025	2 pages
Outcomes assessed:
Tutorial quiz	Quiz 2 Written in class quiz	15%	Week 07 Due date: 08 Apr 2025 at 12:00	90 minutes
Outcomes assessed:
Assignment	Analysis critique Written task	20%	Week 08 Due date: 15 Apr 2025 at 23:59 Closing date: 25 Apr 2025	4 pages
Outcomes assessed:
Assignment	Research plan Written task	30%	Week 10 Due date: 06 May 2025 at 23:59 Closing date: 16 May 2025	5 pages
Outcomes assessed:
= AI allowed ?

Assessment summary

Quizzes: two in-person invigilated tasks where students are presented with one or more scenarios (context, code and output) from which they need to:
- describe the most appropriate analyses for the given context
- demonstrate understanding of output from statistical software
- interpret and explain appropriate statistical tests and methods
- draw relevant and appropriate conclusions
Assignments
- Statistical design critique: a written critique of an experimental design published in the scientific literature.
- Analysis critique: a written critique of a statistical analysis published in the scientific literature.
- Research plan: a written task in which students apply what they have learnt about rigorous experimental design to prepare a research plan for an honours project OR write a review of some recent literature on the challenges with study design.

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	At HD level, a student demonstrates a flair for the subject as well as a detailed and comprehensive understanding of the unit material. A ‘High Distinction’ reflects exceptional achievement and is awarded to a student who demonstrates the ability to apply their subject knowledge and understanding to produce original solutions for novel or highly complex problems and/or comprehensive critical discussions of theoretical concepts.
Distinction	75 - 84	At DI level, a student demonstrates an aptitude for the subject and a well-developed understanding of the unit material. A ‘Distinction’ reflects excellent achievement and is awarded to a student who demonstrates an ability to apply their subject knowledge and understanding of the subject to produce good solutions for challenging problems and/or a reasonably well-developed critical analysis of theoretical concepts.
Credit	65 - 74	At CR level, a student demonstrates a good command and knowledge of the unit material. A ‘Credit’ reflects solid achievement and is awarded to a student who has a broad general understanding of the unit material and can solve routine problems and/or identify and superficially discuss theoretical concepts.
Pass	50 - 64	At PS level, a student demonstrates proficiency in the unit material. A ‘Pass’ reflects satisfactory achievement and is awarded to a student who has threshold knowledge.
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.

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	Surveying and monitoring	Lecture (2 hr)
	Surveying and monitoring	Seminar (2 hr)
	Introduction to R	Workshop (2 hr)
Week 02	Testing for differences between means	Lecture (2 hr)
	Testing for differences between means	Seminar (2 hr)
	Testing for differences between means	Workshop (2 hr)
Week 03	Experimental design	Lecture (2 hr)
	Experimental design	Seminar (2 hr)
	Experimental design	Workshop (2 hr)
Week 04	Generalised linear models	Lecture (2 hr)
Week 04	Generalised linear models	Seminar (2 hr)
Week 05	Random Effect Models	Lecture (2 hr)
	Random Effect Models	Seminar (2 hr)
	Linear and generalised linear (mixed) models	Workshop (2 hr)
Week 06	Statistical machine learning	Lecture (2 hr)
	Statistical machine learning	Seminar (2 hr)
	Statistical machine learning	Workshop (2 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. create aims and hypotheses and design a rigorous scientific experiment to test an hypothesis
LO2. describe the key elements which make up a valid experimental design
LO3. work in a responsible, professional, culturally competent and ethical manner both independently and collaboratively in teams
LO4. select and apply the appropriate statistical tests to analyse experimental data sets
LO5. interpret the outcomes of statistical analyses of experimental data and evaluate the hypothesis in view of the results
LO6. critique design and analysis in scientific literature
LO7. communicate conclusions effectively using a range of media to a variety of audiences

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.

Updates to the timetable (shifting the workshop from Wednesday afternoon to Tuesday after the seminar) and clearer structure to the Canvas page.

Additional information

Science student portal

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

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

SCIE4002: Experimental Design and Data Analysis

Semester 1a, 2025 [Block mode] - Camperdown/Darlington, Sydney

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

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect