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 unit 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. The advanced version of the unit looks more at the mathematical underpinnings of the statistics used.

Unit details and rules

Unit code	ATHK1901
Academic unit	Psychology Academic Operations
Credit points	6
Prohibitions ?	ATHK1001
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	75 or above in HSC Mathematics Standard or equivalent
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Bruce Burns, bruce.burns@sydney.edu.au
Lecturer(s)	Bruce Burns, bruce.burns@sydney.edu.au
Lecturer(s)	Alex Holcombe, alex.holcombe@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final Exam See Canvas for details.	30%	Formal exam period	80 minutes
Outcomes assessed:
Online task	Mastery Quizzes See Canvas for details.	5%	Ongoing	See Canvas for details.
Outcomes assessed:
Participation	Tutorial Participation See Canvas for details.	5%	Ongoing	See Canvas for details.
Outcomes assessed:
Participation	Lecture Engagement See Canvas for details.	5%	Ongoing	See Canvas for details.
Outcomes assessed:
Online task	Mastery Quiz (Week 3) See Canvas for details. #earlyfeedback	0%	Week 03	See Canvas for details.
Outcomes assessed:
Assignment	Assignment 1 See Canvas for details.	20%	Week 06 Due date: 28 Mar 2024 at 23:59 Closing date: 25 Apr 2025	1000 words
Outcomes assessed:
Supervised test ?	In-Semester Test See Canvas for details.	20%	Week 07 Due date: 12 Apr 2024 at 12:00	50 minutes
Outcomes assessed:
Assignment	Assignment 2 See Canvas for details.	15%	Week 11 Due date: 06 May 2024 at 23:59 Closing date: 27 May 2024	750 words
Outcomes assessed:
= hurdle 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

If you miss the Final Exam, you may apply for Special Consideration, from which the only outcome is a 'replacement', which will be held in the University's Replacement Exam period. If you miss the Final Exam and are not approved for 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).

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 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.

Support for students

The Support for Students Policy 2023 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 2023. 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. Regression analysis	Lecture (3 hr)
Week 05	Hypotheses and statistical testing using Excel	Tutorial (1 hr)
Week 06	1. Regression analysis issues; 2. Program evaluation & causality; 3. Non-causality & Summing up	Lecture (3 hr)
Week 06	Regression analysis	Tutorial (1 hr)
Week 07	Correlation versus causation; Nuisance variables, confound variables, and time	Lecture (3 hr)
Week 07	The Central Limit theorem	Tutorial (1 hr)
Week 08	Advanced correlation and causation and dealing with spurious correlations; Advanced correlation and causation and dealing with spurious correlations 2	Lecture (3 hr)
Week 08	Causal models and scientific studies	Tutorial (1 hr)
Week 09	Hypothesis testing, medical testing, and excess testing; Arguments and logic basics	Lecture (3 hr)
Week 09	Necessary and sufficient conditions	Tutorial (1 hr)
Week 10	Towards real-world logical arguments; Towards real-world logical arguments part 2	Lecture (3 hr)
Week 10	Evaluating simple arguments	Tutorial (1 hr)
Week 11	Real-world arguments, paragraphs, and induction; Real-world arguments, paragraphs, and induction part 2	Lecture (3 hr)
Week 11	Implicit premises and induction	Tutorial (1 hr)
Week 12	Logic in science and fallacious arguments; Logic in science and fallacious arguments part 2	Lecture (3 hr)
Week 12	The effect of prior beliefs on reasoning	Tutorial (1 hr)
Week 13	Rhetoric, fallacies in conversations, and good conversations	Lecture (3 hr)
Week 13	No Description	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.

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

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

Early feedback task

Assessment summary

Assessment criteria

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

Media

Student links

About us

Connect

Current students

ATHK1901: Analytical Thinking (Advanced)

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

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

Media

Student links

About us

Connect