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Unit of study_

ATHK1901: Analytical Thinking (Advanced)

Semester 1, 2023 [Normal day] - Camperdown/Darlington, Sydney

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
Assumed knowledge

75 or above in HSC Mathematics Standard or equivalent

Available to study abroad and exchange students


Teaching staff

Coordinator Bruce Burns,
Lecturer(s) Alex Holcombe,
Type Description Weight Due Length
Supervised exam
Final Exam
See Canvas for details.
30% Formal exam period 80 minutes
Outcomes assessed: LO1 LO2 LO4 LO5 LO6 LO7
Assignment Assignment 1
See Canvas for details.
15% Week 06
Due date: 31 Mar 2023 at 23:59

Closing date: 28 Apr 2023
750 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO8
Supervised test
In-Semester Test
See Canvas for details.
20% Week 07
Due date: 06 Apr 2023 at 14:00
50 minutes
Outcomes assessed: LO1 LO2 LO4 LO5
Assignment Assignment 2
See Canvas for details.
20% Week 11
Due date: 08 May 2023 at 23:59

Closing date: 29 May 2023
1000 words
Outcomes assessed: LO5 LO6 LO8
Online task Online Mastery Quizzes
See Canvas for details.
5% Weekly May be attempted multiple times.
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO2
Participation Participation and Attendance in Tutorials
See Canvas for details.
5% Weekly N/A
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Participation Engagement with Lecture Presentations
See Canvas for details.
5% Weekly N/A
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2

Assessment summary

Final exam: This is a compulsory task which you need to attempt, or you will receive an absent fail, but no minimum mark is required.


More detailed information for each assessment can be found 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


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


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.


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.


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.


0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

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.

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.

WK Topic Learning activity Learning outcomes
Week 01 1. Introduction to analytical thinking; 2. Why study statistics?; 3. Descriptive statistics Lecture (3 hr) LO1
Orientation Tutorial (1 hr)  
Week 02 1. Deceptive statistics; 2. Correlation; 3. Basic probability Lecture (3 hr) LO1
Using Excel for descriptive statistics Tutorial (1 hr) LO1
Week 03 1. Problems with probability; 2. Collecting data; 3. Data issues Lecture (3 hr) LO1 LO3 LO4
Calculating statistics Tutorial (1 hr) LO1 LO3
Week 04 1. Inference & the normal distribution; 2. The central limit theorem; 3. Hypothesis testing Lecture (3 hr) LO2 LO5
Instructions for Assignment 1 Tutorial (1 hr) LO1 LO2 LO8
Week 05 1. Statistical tests; 2. Analysis of categorical data; 3. Regression analysis Lecture (3 hr) LO2 LO4
Hypotheses and statistical testing using Excel Tutorial (1 hr) LO2 LO3
Week 06 1. Regression analysis issues; 2. Program evaluation & causality; 3. Non-causality & Summing up Lecture (3 hr) LO2
Understanding hypotheses and statistical testing Tutorial (1 hr) LO2 LO3
Week 07 Correlation versus causation; Nuisance variables, confound variables, and time Lecture (3 hr) LO4 LO5
Regression analysis Tutorial (1 hr) LO2 LO3
Week 08 Advanced correlation and causation and dealing with spurious correlations; Advanced correlation and causation and dealing with spurious correlations 2 Lecture (3 hr) LO4 LO5
Causal models and scientific studies Tutorial (1 hr) LO4 LO5
Week 09 Hypothesis testing, medical testing, and excess testing; Arguments and logic basics Lecture (3 hr) LO4 LO5 LO6
Necessary and sufficient conditions Tutorial (1 hr) LO6
Week 10 Towards real-world logical arguments; Towards real-world logical arguments part 2 Lecture (3 hr) LO6
Evaluating simple arguments Tutorial (1 hr) LO6
Week 11 Real-world arguments, paragraphs, and induction; Real-world arguments, paragraphs, and induction part 2 Lecture (3 hr) LO6 LO7
Implicit premises and induction Tutorial (1 hr) LO7
Week 12 Logic in science and fallacious arguments; Logic in science and fallacious arguments part 2 Lecture (3 hr) LO7
The effect of prior beliefs on reasoning Tutorial (1 hr) LO7
Week 13 Rhetoric, fallacies in conversations, and good conversations Lecture (3 hr) LO7
No Description Tutorial (1 hr) LO7

Attendance and class requirements

Attendance at at least 80% of tutorials will be required in order to obtain a participation mark for tutorials. Viewing of recorded lectures will be recorded and contribute to your mark for engagement
with lectures.

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

Short readings for this unit can be accessed via Canvas. Their availablity will be announced when appropriate.

There is one recommended (but not required) text which will help you with the Data Concepts and Analysis section:
Wheelan, C. (2013). Naked Statistics: Stripping the dread from the data . New York: Norton.

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. 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

This section outlines changes made to this unit following staff and student reviews.



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