Skip to main content
Unit of study_

MATH1005: Statistical Thinking with Data

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.


Academic unit Mathematics and Statistics Academic Operations
Unit code MATH1005
Unit name Statistical Thinking with Data
Session, year
Intensive January, 2022
Attendance mode Block mode
Location Camperdown/Darlington, Sydney
Credit points 3

Enrolment rules

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 and contact details

Coordinator Daniel Hauer,
Lecturer(s) Sean Skinner ,
Nicholas Hindley,
Administrative staff Mathematics and Statistics Student Services Office, Carslaw 520
Type Description Weight Due Length
Assignment Revision Quizzes
Multiple choice revision questions
10% Progressive 30 minutes each
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Assignment group assignment Project 1
Data projects in a team.
20% Week 03
Due date: 30 Jan 2022

Closing date: 06 Feb 2022
2 weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Assignment Project 2
Data project.
20% Week 05
Due date: 13 Feb 2022

Closing date: 20 Feb 2022
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Final exam (Record+) Type B final exam Final exam
Multiple Choice and Extended Answer Questions
50% Week 06
Due date: 14 Feb 2022
1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
group assignment = group assignment ?
Type B final exam = Type B final exam ?

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 quiz 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.

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

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Progressive Design of Experiments: Controlled Experiments + Observational Studies Lecture (2 hr) LO1 LO2
Data & Graphical Summaries: Qualitative Data + Quantitative Data Lecture (2 hr) LO3
Numerical Summaries: Centre + Spread Lecture (2 hr) LO3
Normal Model: Normal Curve + Measurement Error Lecture (2 hr) LO4
Linear Model: Scatter Plot & Correlation + Regression Line Lecture (2 hr) LO5
Understanding Chance: Chance + More Chance Lecture (2 hr) LO6
Chance Variability: Law of Averages + The Box Model Lecture (2 hr) LO6
Chance Variability / Sample Surveys: Normal Approximation + Sample Surveys Lecture (2 hr) LO4 LO6 LO9
Sample Surveys: The Box Model for Sample Surveys + Bootstrapping & Confidence Intervals Lecture (2 hr) LO6
Test for a Proportion: Hypothesis Testing + Proportion Test Lecture (2 hr) LO7 LO8
Tests for a Mean: Accuracy of Means + Z and T Tests Lecture (2 hr) LO7 LO8
Test for a Relationship: 2 Sample T Test Lecture (2 hr) LO7 LO8
Lab 1 Computer laboratory (2 hr) LO1 LO2 LO10
Lab 2 Computer laboratory (2 hr) LO3
Lab 3 Computer laboratory (2 hr) LO3
Lab 4 Computer laboratory (2 hr) LO4
Lab 5 Computer laboratory (2 hr) LO5
Lab 6 (Project Work) Computer laboratory (2 hr) LO10
Lab 7 (Project Presentation) Computer laboratory (2 hr) LO10
Lab 8 Computer laboratory (2 hr) LO6
Lab 9 Computer laboratory (2 hr) LO6
Lab 10 Computer laboratory (2 hr) LO7 LO8
Lab 11 Computer laboratory (2 hr) LO7 LO8
Lab 12 Computer laboratory (2 hr) LO7 LO8

Attendance and class requirements

Due to the exceptional circumstances caused by the COVID-19 pandemic, attendance requirements for this unit of study have been amended. Where online tutorials/workshops/virtual laboratories have been scheduled, students should make every effort to attend and participate at the scheduled time. Penalties will not be applied if technical issues, etc. prevent attendance at a specific online class.

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

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
Weight of projects towards final mark increased. Weight of final exam decreased.

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.