Outline

Overview

Analytical Thinking covers aspects of research design, interpretation of data, analysis, logic, and thinking processes. It is comprised of three sections: Data Concepts and Analysis; Logic and Basic Arguments; and Research and Everyday Reasoning. The section on Data Concepts and Analysis covers aspects of research design, data collection, and introduces basic forms of hypothesis testing and statistical tests. The Logic and Basic Arguments section covers material ranging from valid and invalid forms of argument and errors in reasoning to critiques of arguments presented in case studies. The Research and Everyday Reasoning section examines how arguments and scientific evidence are presented and interpreted in the media, society, and interpersonal interactions. Together, the three course components teach foundational skills necessary for carrying out meaningful academic discussions, arguments, and research studies, which may be applied to any area of scholarly enquiry.

Unit details and rules

Academic unit	Psychology Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	ATHK1901
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Bruce Burns, bruce.burns@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam See the 'Assessment summary' below and Canvas site for details.	55%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	Mastery quizzes See the 'Assessment summary' below and Canvas site for details.	5%	Ongoing	See Canvas for details.	AI allowed
Outcomes assessed:
Contribution	Tutorial participation See the 'Assessment summary' below and Canvas site for details.	5%	Ongoing	See Canvas for details.	AI allowed
Outcomes assessed:
Contribution	Lecture Engagement See the 'Assessment summary' below and Canvas site for details.	5%	Ongoing	See Canvas for details.	AI allowed
Outcomes assessed:
Out-of-class quiz	Mastery Quiz (Week 3) One of the Mastery Quizzes.	0%	Week 03	See Canvas for details.	AI allowed
Outcomes assessed:
Written work	Assignment 1 See the 'Assessment summary' below and Canvas site for details.	20%	Week 06 Due date: 02 Apr 2026 at 23:59 Closing date: 30 Apr 2026	1000 words	AI allowed
Outcomes assessed:
Written work	Assignment 2 See the 'Assessment summary' below and Canvas site for details.	10%	Week 11 Due date: 11 May 2026 at 23:59 Closing date: 01 Jun 2026	750 words	AI allowed
Outcomes assessed:
= hurdle task ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Lecture Engagement: This is based on correctly answering quiz questions during in person lectures. If you have a scheduled class clash that prevents attendance at the relevant lecture then we will arrange an alternative assessment.
Tutorial Participation: Based on attendance and active participation in tutorials
Mastery Quizzes: Weekly online quizzes based on that week's content
Assignment 1: Written assignment focused on data analysis.
Assignment 2: Written assigned focused on logic and using evidence
Final Exam: If you do not attempt the Final Exam, you will need to apply for Special Consideration, from which the only outcome is a 'replacement', and the Replacement Exam would be completed in the University's Replacement Exam period. If you do not attempt the Final Exam and are not awarded Special Consideration, you will receive an Absent Fail (AF) grade for this unit, as the Final Exam is a compulsory assessment.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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	1. Introduction to analytical thinking; 2. Why study statistics?; 3. Descriptive statistics	Lecture (3 hr)
Week 01	Orientation	Tutorial (1 hr)
Week 02	1. Deceptive statistics; 2. Correlation; 3. Basic probability	Lecture (3 hr)
Week 02	Using Excel for descriptive statistics	Tutorial (1 hr)
Week 03	1. Problems with probability; 2. Collecting data; 3. Data issues	Lecture (3 hr)
Week 03	Calculating statistics	Tutorial (1 hr)
Week 04	1. Inference & the normal distribution; 2. The central limit theorem; 3. Hypothesis testing	Lecture (3 hr)
Week 04	Question about Assignment 1/ Hypothesis testing	Tutorial (1 hr)
Week 05	1. Statistical tests 2. Analysis of categorical data; 3. Program evaluation;	Lecture (3 hr)
Week 05	Hypotheses and statistical testing using Excel	Tutorial (1 hr)
Week 06	Correlation versus causation; Nuisance variables, confound variables, and time	Lecture (3 hr)
Week 06	The Central Limit theorem	Tutorial (1 hr)
Week 07	Advanced correlation and causation and dealing with spurious correlations; Advanced correlation and causation and dealing with spurious correlations 2	Lecture (3 hr)
Week 07	Causal models and scientific studies	Tutorial (1 hr)
Week 08	Hypothesis testing, medical testing, and excess testing; Arguments and logic basics	Lecture (3 hr)
Week 08	Necessary and sufficient conditions	Tutorial (1 hr)
Week 09	Towards real-world logical arguments; Towards real-world logical arguments part 2	Lecture (3 hr)
Week 09	Evaluating simple arguments	Tutorial (1 hr)
Week 10	Real-world arguments, paragraphs, and induction; Real-world arguments, paragraphs, and induction part 2	Lecture (3 hr)
Week 10	Implicit premises and induction	Tutorial (1 hr)
Week 11	Logic in science and fallacious arguments; Logic in science and fallacious arguments part 2	Lecture (3 hr)
Week 11	The effect of prior beliefs on reasoning	Tutorial (1 hr)
Week 12	Rhetoric, fallacies in conversations, and good conversations	Lecture (3 hr)
Week 12	Rhetoric and Informal fallacies	Tutorial (1 hr)
Week 13	Analogous reasoning; Fallacies: Appeal to ignorance and excluded middle	Lecture (3 hr)
Week 13	Analogous reasoning	Tutorial (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

These will be specified on the reading list.

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. Demonstrate a basic conceptual understanding of descriptive statistics used across disciplines.
LO2. Demonstrate a basic conceptual understanding of inferential statistics used across disciplines, though not of their mathematical underpinnings.
LO3. Demonstrate basic skills in computing and data handling
LO4. Understand and evaluate the quality of data based on its sources and the different methods used to collect it.
LO5. Identify ways of approaching the exploration of a research question and understand potential sources of bias in information sources.
LO6. Demonstrate a basic understanding of how logic can help analytical thinking and use it to analyze problems, within your discipline and across disciplines.
LO7. Demonstrate an understanding of basic processes of thinking and why these could lead to errors in reasoning, the evaluation of data and of learning.
LO8. Communicate the results of data analysis and research appropriately through written work

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.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ATHK1001: Analytical Thinking

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links