MATH1005: Semester 1, 2025
Skip to main content
Unit outline_

MATH1005: Statistical Thinking with Data

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

In a data-rich world, global citizens need to problem solve with data and evidence based decision-making is essential in every field of research and work. This unit equips you with the foundational statistical thinking to become a critical consumer of data. You will learn to think analytically about data and to evaluate the validity and accuracy of any conclusions drawn. Focusing on statistical literacy, the unit covers foundational statistical concepts, including the design of experiments, exploratory data analysis, sampling and tests of significance.

Unit details and rules

Academic unit Mathematics and Statistics Academic Operations
Credit points 3
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
MATH1015 or MATH1905 or STAT1021 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or DATA1901
Assumed knowledge
? 

HSC Mathematics Advanced or equivalent

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Tiangang Cui, tiangang.cui@sydney.edu.au
Lecturer(s) Tiangang Cui, tiangang.cui@sydney.edu.au
The census date for this unit availability is 31 March 2025
Type Description Weight Due Length
Supervised exam
? 
Final exam
Multiple choice and written calculations
60% Formal exam period 1 hour
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Online task AI Allowed Weekly online quizzes 3-10
Weekly online quizzes
6% Multiple weeks 1 hour per week
Outcomes assessed: LO1 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Online task Early Feedback Task AI Allowed Weekly online quizzes 1-2
#earlyfeedbacktask
2% Week 03
Due date: 16 Mar 2025 at 23:59

Closing date: 16 Mar 2025
1 hour per quiz
Outcomes assessed: LO1 LO2
Short release assignment AI Allowed Assignment 1
Computational data analysis
5% Week 04
Due date: 23 Mar 2025 at 23:59

Closing date: 02 Apr 2025
An R markdown report
Outcomes assessed: LO1 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Small test Quiz
Multiple choice
15% Week 07 20 minutes
Outcomes assessed: LO2 LO6 LO5 LO4 LO3
Short release assignment AI Allowed Assignment 2
Computational data analysis
10% Week 10
Due date: 11 May 2025 at 23:59

Closing date: 21 May 2025
An R markdown report
Outcomes assessed: LO1 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Participation Workshops
Participation in workshops
2% Weekly 50 minutes per week
Outcomes assessed: LO1 LO8 LO7 LO6 LO5 LO4 LO3 LO2
AI allowed = AI allowed ?
early feedback 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

  • Assignments:  There are two short release assignments. Your work for each assignment must be submitted electronically via Canvas by the deadline. Note that your submission will not be marked if it is illegible or if it is submitted sideways or upside down. It is your responsibility to check that your assignment has been submitted correctly and that it is complete (check that you can view each page). Late submissions will receive a penalty. A mark of zero will be awarded for all submissions more than 10 days past the original due date. Further extensions past this time will not be permitted. The maximum extension you can be awarded through Special Consideration for the assignments is 7 calendar days. If you are affected for more than 7 calendar days you will be granted a mark adjustment. This means that your final exam mark will count instead for the assignment mark. The closing date for submissions (with a late penalty) is the same for all students. It is not changed if you are granted an extension. This allows for timely release of the marks and feedback. Note that the assignments are not eligible for a Simple Extension through the Special Consideration system since they are short release assignments (released to you to complete within 10 working days).
  • Quiz: One quiz will be held in-person on campus during Week 7. You must sit the quiz at the time and location that appears as Assessment in your timetable. If you are unable to sit the quiz at that time, then you must apply for Special Consideration or Special Arrangements. Quiz feedback will be returned through Canvas.
  • Weekly Online Quizzes: There are ten weekly online quizzes (through Canvas and equally weighted) and the marks for the best eight count. The first two are used for the Early Feedback Task. Each online quiz consists of a set of randomized questions. You should not apply for special consideration for the quizzes. The better mark principle will apply for the total 8% - i.e. if your overall exam mark is higher, then your 8% for the weekly online quizzes will come from your exam. The deadline for completion of each quiz is 23:59 Sunday (starting in week 3). The precise schedule for the quizzes is found on Canvas. We recommend that you follow the due dates outlined above to gain the most benefit from these quizzes.
  • Workshop Participation: This is a satisfactory/non-satisfactory mark assessing whether or not you participate in class activities during the workshops. It is 0.25 marks per workshop class up to 8 workshops (there are 11 workshops).
  • Final Examination: The final exam for this unit is compulsory and must be attempted. Failure to attempt the final exam will result in an AF grade for the course. Further information about the exam will be made available at a later date on Canvas. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

Detailed information for each assessment can be found on Canvas.

Even though the use of AI is allowed for some assessments, it is better for your learning to do your own work to complete your assessments.

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

Representing complete or close to complete mastery of the material.

Distinction

75 - 84

Representing excellence, but substantially less than complete mastery.

Credit

65 - 74

Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.

Pass

50 - 64

Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.

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.

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.

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

WK Topic Learning activity Learning outcomes
Week 01 Graphical summaries Lecture (2 hr) LO1 LO2 LO3
Week 02 Numerical summaries Lecture and tutorial (2 hr) LO1 LO2 LO3
Week 03 Numerical summaries and normal model Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 04 Linear models Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO9
Week 05 Linear models, understanding chance and variability Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 06 Understanding chance and variability Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 07 Understanding chance and variability Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 08 The Central Limit theorem, confidence intervals Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 09 Test for a proportion Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO9
Week 10 Test for a mean and tests for relationships Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 11 Test for a difference of two proportions Lecture and tutorial (3 hr) LO7 LO8
Week 12 Chi-squared tests and p-values Lecture and tutorial (2 hr) LO7 LO8
Week 13 Revision Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9

Attendance and class requirements

  • Lecture attendance: You are expected to attend lectures. If you do not attend lectures you should at least follow the lecture recordings available through Canvas. 
  • Workshop attendance: Workshops (one per week) start in Week 2. You should attend the workshop given on your personal timetable. Attendance at workshops and participation will be recorded to determine the participation mark. Your attendance will not be recorded unless you attend the workshop in which you are enrolled. We strongly recommend you attend workshops regularly to keep up with the material and to engage with the workshop questions. 

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 3 credit point unit, this equates to roughly 60-75 hours of student effort in total.

Required readings

There are no required readings. However, we will loosely follow Statistics, Freedman, Pisani, and Purves (2007). Examples of how to get access to the text book:

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. articulate the importance of statistics in a data-rich world
  • LO2. identify the study design behind a dataset and how the study design affects context specific outcomes
  • LO3. produce, interpret and compare graphical and numerical summaries in R
  • LO4. apply the normal approximation to data, with consideration of measurement error
  • LO5. model the relationship between 2 variables using linear regression
  • LO6. use the box model to describe chance and chance variability, including sample surveys and the central limit theorem
  • LO7. given real multivariate data and a problem, formulate an appropriate hypothesis and perform a hypothesis test
  • LO8. interpret the p-value, conscious of the various pitfalls associated with testing
  • LO9. critique the use of statistics in media and research papers, with attention to confounding and bias
  • LO10. perform data exploration in a team, and communicate the findings via oral and oral reproducible reports

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

No changes have been made since this unit was last offered.
  • Lectures: Lectures are face-to-face and streamed live with online access from Canvas.
  • Workshops: Workshops are small classes in which you are expected to work through questions from the tutorial sheet.
  • Ed Discussion forum: https://edstem.org

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

This unit of study outline was last modified on 10 Feb 2025.

To help you understand common terms that we use at the University, we offer an online glossary.