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Unit of study_

MATH1005: Statistical Thinking with Data

Semester 1, 2022 [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

Unit code MATH1005
Academic unit Mathematics and Statistics Academic Operations
Credit points 3
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


Teaching staff

Coordinator Jie Yen Yen Fan,
Lecturer(s) Andy Tran,
Type Description Weight Due Length
Final exam (Record+) Type B final exam Exam
Multiple Choice and Extended Answer Questions
50% Formal exam period 1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Assignment group assignment Project 1
See Canvas
20% Week 08
Due date: 14 Apr 2022 at 23:59

Closing date: 26 Apr 2022
4 weeks
Outcomes assessed: LO1 LO10 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Project 2
See Canvas
20% Week 12
Due date: 20 May 2022 at 23:59

Closing date: 30 May 2022
4 weeks
Outcomes assessed: LO1 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Revision Quiz
Multiple Choice Revision Questions
10% Weekly Weekly Revision Quiz
Outcomes assessed: LO1 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
group assignment = group assignment ?
Type B final exam = Type B final exam ?

Assessment summary

  • Weekly quizzes: The best 10 out of 13 quizzes count. The better mark principle will be used for the weekly quizzes so do not submit an application for Special Consideration or Special Arrangements if you miss a quiz. The better mark principle means that the total quiz mark counts if and only if it is better than or equal to your exam mark. If your quiz mark is less than your exam mark, the exam mark will be used for that portion of your assessment instead.
  • Projects: Details about the projects and instructions for submission will be given on Canvas. Penalties apply for late submission. 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.
  • Final Examination: Details will be provided later. 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.
  • Simple extensions: No simple extensions are given in first year units in the School of Mathematics and Statistics.

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

Representing complete or close to complete mastery of the material.


75 - 84

Representing excellence, but substantially less than complete mastery.


65 - 74

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


50 - 64

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


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 Design of Experiments: Controlled experiments + Observational studies Lecture (2 hr) LO1 LO2
Lab 1 Computer laboratory (1 hr) LO1 LO2
Week 02 Data & Graphical Summaries: Qualitative + Quantitative data Lecture (2 hr) LO3
Lab 2 Computer laboratory (1 hr) LO3 LO10
Week 03 Data & Graphical Summaries: Centre + Spread Lecture (2 hr) LO3
Lab 3 Computer laboratory (1 hr) LO3 LO10
Week 04 Numerical Summaries: Normal Model + Reproducible Reports Lecture (2 hr) LO1 LO4 LO9
Lab 4 Computer laboratory (1 hr) LO3 LO10
Week 05 Linear Model: Scatter Plot & Correlation + Regression line Lecture (2 hr) LO5
Lab 5 Computer laboratory (1 hr) LO5
Week 06 Understanding Chance: Chance & Simulations Lecture (2 hr) LO6
Lab 6 Computer laboratory (1 hr) LO6 LO10
Week 07 Chance Variability: Law of averages and sums + The Box Model Lecture (2 hr) LO6
Lab 7 Computer laboratory (1 hr) LO6 LO10
Week 08 Chance Variability: Normal approximation Lecture (2 hr) LO4
Lab 8 Computer laboratory (1 hr) LO4
Week 09 Sample Surveys: The Box Model for Sample Surveys & Simulations Lecture (2 hr) LO1 LO6 LO9
Lab 9 Computer laboratory (1 hr) LO6 LO9
Week 10 Testing: Hypothesis testing & Z Test Lecture (2 hr) LO6 LO7 LO8
Lab 10 Computer laboratory (1 hr) LO7 LO8
Week 11 Testing: One and Two Sample T Tests Lecture (2 hr) LO7 LO8
Lab 11 Computer laboratory (1 hr) LO7 LO8
Week 12 Testing: Other Tests Lecture (2 hr) LO7 LO8
Lab 12 Computer laboratory (1 hr) LO7 LO8
Week 13 Revision Lecture (2 hr)  

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. 
  • Lab attendance: Labs (one per week) start in Week 1. You should attend the lab given on your personal timetable. Attendance at labs will be recorded. Your attendance will not be recorded unless you attend the lab in which you are enrolled. While there is no penalty if 80% attendance is not met we strongly recommend you attend labs regularly to keep up with the material and to engage with the lab questions. Since there is no assessment associated with the labs do not submit an application for Special Consideration or Special Arrangements for missed labs.

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

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

No changes have been made since the unit was last offered.
  • Lectures: Lectures are online and live. Access from Canvas or Ed.
  • Labs: Labs are small classes in which you are expected to work through questions from the tutorial sheet.
  • Labs: Labs start in week 1. You should attend the lab given on your personal timetable. Attendance at labs will be recorded. Your attendance will not be recorded unless you attend the lab in which you are enrolled. If you are absent from a lab, do not apply for Special Consideration or Special Arrangement, since there is no assessment associated with the missed lab.
  • Ed Discussion forum:

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.


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.