MZB103 Introduction to Statistics
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | MZB103 |
|---|---|
| Antirequisite(s): | SEB113 |
| Credit points: | 6 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $296 |
| Domestic tuition unit fee | $1,908 |
| International unit fee | $2,436 |
Unit Outline: Semester 1 - 6 Week A 2026, Gardens Point, Internal
| Unit code: | MZB103 |
|---|---|
| Credit points: | 6 |
| Anti-requisite: | SEB113 |
| Coordinator: | Helen Thompson | helen.thompson@qut.edu.au |
Overview
MZB103 is a foundational science unit for developing core skills in statistics that underpins all modern scientific practice and covers topics relevant to later science units. Thus this unit serves as a foundation and prerequisite for many subsequent units in the science degree. The applied approach in this unit develops topics through examples in science which are accessible to students from a range of disciplines.
Learning Outcomes
On successful completion of this unit you will be able to:
- Recall core foundational statistics concepts and translate them to new scientific contexts
- Critically select and apply statistical techniques and interpret statistical outputs to solve scientific problems
- Communicate in written and graphical formats to scientists and the broader community using appropriate conventions
Content
In this unit you will explore the following topics:
- Week 1: collecting data, processing data, planning your statistical analysis
- Week 2: one-sample tests and confidence intervals
- Week 3: two-sample and paired tests and confidence intervals
- Week 4: one-proportion and two-proportion tests and confidence intervals
- Week 5: chi-square tests
Note: This unit does not teach or assess coding skills, but use of R statistical software is required to support statistics learning.
Learning Approaches
Indicative Workload
The total volume of learning in MZB103 is 75 hours, consistent with the Australian Qualifications Framework, which describes the typical time required for a student with no prior achievement of the unit learning outcomes to develop the required knowledge and skills. An indicative breakdown of this workload is provided below.
Timetabled contact - 19 hours
- Lectures: 10 hours (2 hours x 5 weeks)
- Workshops: 5 hours (1 hour x 5 weeks)
- Assessment support: 4 hours (2 hours x 2 weeks)
Assessment - 28.5 hours
- Formative quizzes: 5 hours (1 hour x 5 weeks)
- Statistics portfolio contributions: 12 hours (3 hours x 4 weeks)
- Exam preparation: 10 hours (2 hours x 5 weeks)
- Invigilated exam: 1.5 hours
Self-directed learning - 27.5 hours
- Independent study across Weeks 1-5 (5.5 hours x 5 weeks), including:
- Pre-workshop preparation (2 hours x 5 weeks, potentially collaborative)
- Independent practice and consolidation
- Reading and review of lecture materials
Learning Design
This unit uses a structured, scaffolded "I do -- we do -- you do" model of learning. Core concepts and methods are first introduced and demonstrated by the lecturer in weekly lectures ("I do"). Students then apply these concepts in small-group workshops through guided activities, discussion, and peer explanation, with the teacher facilitating learning and providing support ("we do"). Students subsequently apply the same concepts independently in assessment tasks ("you do").
Lectures are delivered as 2-hour face-to-face interactive sessions, focussing on explanation of core concepts and demonstration of worked examples that model expected approaches.
Workshops are 1-hour face-to-face small-group classes designed to support active engagement and participation. In Week 1, workshops are teacher-led and introduce the workshop structure, expectations for participation, and the use of R statistical software in Google Colab. In Weeks 2-5, workshops incorporate structured student-led explanation of assigned activities, with the teacher guiding discussion, providing clarification, and addressing misconceptions as required.
Learning is further supported through self-directed study, including pre-workshop preparation, independent practice, and review of lecture materials. Students are encouraged to engage collaboratively with peers during preparation and workshops, while recognising that assessment tasks require individual demonstration of learning.
Optional assessment support sessions are scheduled during the teaching period to provide targeted assistance, supporting students' learning and preparation for assessment.
In addition, FREE STEM Support for Science Students workshops are also available throughout the year.
Feedback on Learning and Assessment
Feedback is provided throughout the unit to support students' learning, development of statistical understanding, and preparation for assessment.
Students receive ongoing formative feedback during face-to-face workshop classes, where the teacher provides guidance, clarification, and feedback on students' approaches as they work through activities. Workshops also incorporate collaborative discussion and peer explanation, enabling students to receive immediate informal feedback through questioning, comparison of approaches, and shared problem-solving.
Additional feedback is provided through weekly formative quizzes, which offer timely feedback to support self-assessment and help students identify areas requiring further practice. Feedback is also provided on portfolio assessment submissions, supporting students' understanding of assessment expectations and areas for improvement.
Portfolio assessment is structured to support learning over time. Portfolio Part 2 requires students to reflect on feedback and marks received for Portfolio Part 1, encouraging students to evaluate their learning and demonstrate development across the teaching period. Marks awarded for summative assessment items provide further feedback on students' achievement of the unit learning outcomes.
Exam feedback is available during the review of grade period by appointment with the unit coordinator.
An online communication channel will be available for you to engage with your peers and teaching staff. Teaching staff will review the channel at least once per week to confirm or correct information, respond to outstanding questions, and provide guidance on common issues arising in the unit. Participation in the channel provides opportunities to receive and give feedback through posting questions, responding to peers, and reviewing posts.
Assessment
Overview
The assessment items in this unit are designed to evaluate your level of achievement of the unit learning outcomes while supporting the progressive development of foundational statistical knowledge and skills through a range of assessment activities.
Weekly Online Quiz (Formative Only)
Weight: 0%
Individual or Group: Individual
Unit Learning Outcomes: ULO1
Formative weekly online multiple choice quizzes with automated feedback will be available, in addition to the summative portfolio assessment, to support your learning. Unlimited attempts are permitted. These quizzes are designed to allow you to self-assess your understanding of foundational statistics concepts. The quiz questions are exemplars for the multiple choice questions on the examination. This is formative-only assessment, the marks do not contribute to your final grade and do not need to be submitted.
Authentication of Learning
To support assessment integrity and individual accountability, students may be required to complete an authentication of learning process, including (but not limited to) cases where a student did not participate in the associated workshop, or where there is inconsistency between performance in portfolio assessment, the exam, or demonstrated understanding during workshop activities.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Statistics Portfolio
The portfolio assesses content from Weeks 1-5 and is submitted in two parts to support progressive learning, timely feedback, and consolidation of knowledge.
Portfolio Part 1 (due Week 3) covers content from Weeks 1-2, allowing students to receive feedback early in the teaching period on their understanding and application of foundational statistical concepts and methods.
Portfolio Part 2 (due Week 5) covers content from Weeks 3-5 and requires students to apply feedback from Portfolio Part 1, supporting reflection, consolidation, and improvement over time in preparation for the examination. The Week 5 component is a brief reflective task linked to the Week 5 workshop and is designed to be completed largely within workshop time, rather than adding substantial additional assessment workload in the week the assessment is due.
Each portfolio submission includes tasks directly related to the associated weekly workshop activities. Each submission also includes a brief reflective component linked to the relevant workshop.
The portfolio questions form exemplars for the short answer questions on the examination.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
The examination will cover all content (content from Weeks 1-5), and assess understanding, application and interpretation of foundational statistics concepts, techniques and models to solve scientific problems.
The examination will consist of 2 sections:
- Section A: Multiple Choice
- Section B: Short Answer
The formative online quizzes form the exemplars for Section A. The statistics portfolio and workshop class worksheets form exemplars for Section B.
You will not be permitted to take a resource sheet into the examination, the examination paper will instead contain a formula sheet, which is the same formula sheet provided to you at the beginning of the teaching period. You are encouraged to use and familiarise yourself with the formula sheet during the teaching period, prior to your examination. You will be permitted to use a calculator of any type.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
The examination will require attendance on QUT campus. The late submission period does not apply, and no extensions are available. if you can’t attend this exam due to special circumstances, you may apply to sit a deferred exam.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Requirements to Study
Costs
There are no out of the ordinary costs associated with the study of this unit.
Resources
Required readings for MZB103 will consist of lecture materials provided on Canvas. Recommended readings will also be provided from the recommended text listed below.
There are many reference texts for this unit, many of which can be located in the library. There are also many online resources such as lecture notes and some e-books that can be found online. Example reference texts are listed below.
All statistical computation will be performed with R statistical software inside the RStudio integrated development environment. Both of these pieces of software are required, are available in QUT computer labs and are available online at no charge for students to install on their personal computers.
Resource Materials
Recommended text(s)
Currell, G & Downman, A (2009) Essential Mathematics and Statistics for Science (2nd ed.) Wiley-Blackwell, Oxford.
Available to view online through QUT Library.
Reference book(s)
Ekstrom, C (2019) R Primer (2nd ed.) Taylor and Francis, Boca Raton, Florida.
Available to view online through QUT Library.
Software
R Statistical Software download for free
RStudio Integrated Development Environment (IDE) download for free
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit, as all classes will be held in ordinary lecture theatres and computer laboratories. Emergency exits and assembly areas will be pointed out in the first few lectures. You are referred to the University policy on health and safety.
http://www.mopp.qut.edu.au/A/A_09_01.jsp
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.ST01 Bachelor of Science
- Develop a broad, multidisciplinary understanding of science and a specialised, in-depth knowledge of at least one discipline.
Relates to: ULO1, ULO2, Statistics Portfolio, Examination (invigilated) - Use higher order thinking skills to design, plan, and conduct investigations and evaluate data to address scientific questions and challenges.
Relates to: ULO2, Statistics Portfolio, Examination (invigilated) - Develop and demonstrate key competencies in scientific practices and relevant technologies.
Relates to: ULO2, Statistics Portfolio, Examination (invigilated) - Communicate scientific findings, concepts and evidence-based reasoning to diverse audiences using a variety of methods.
Relates to: ULO3, Statistics Portfolio
SV02 Bachelor of Science
- Develop a broad, multidisciplinary understanding of science and a specialised, in-depth knowledge of at least one discipline.
Relates to: ULO1, ULO2, Statistics Portfolio, Examination (invigilated) - Use higher order thinking skills to design, plan, and conduct investigations and evaluate data to address scientific questions and challenges.
Relates to: ULO2, Statistics Portfolio, Examination (invigilated) - Develop and demonstrate key competencies in scientific practices and relevant technologies.
Relates to: ULO2, Statistics Portfolio, Examination (invigilated) - Communicate scientific findings, concepts and evidence-based reasoning to diverse audiences using a variety of methods.
Relates to: ULO3, Statistics Portfolio
Unit Outline: Semester 2 - 6 Week C 2026, Gardens Point, Internal
| Unit code: | MZB103 |
|---|---|
| Credit points: | 6 |
| Anti-requisite: | SEB113 |
| Coordinator: | Helen Thompson | helen.thompson@qut.edu.au |
Overview
MZB103 is a foundational science unit for developing core skills in statistics that underpins all modern scientific practice and covers topics relevant to later science units. Thus this unit serves as a foundation and prerequisite for many subsequent units in the science degree. The applied approach in this unit develops topics through examples in science which are accessible to students from a range of disciplines.
Learning Outcomes
On successful completion of this unit you will be able to:
- Recall core foundational statistics concepts and translate them to new scientific contexts
- Critically select and apply statistical techniques and interpret statistical outputs to solve scientific problems
- Communicate in written and graphical formats to scientists and the broader community using appropriate conventions
Content
In this unit you will explore the following topics:
- Week 1: collecting data, processing data, planning your statistical analysis
- Week 2: one-sample tests and confidence intervals
- Week 3: two-sample and paired tests and confidence intervals
- Week 4: one-proportion and two-proportion tests and confidence intervals
- Week 5: chi-square tests
Note: This unit does not teach or assess coding skills, but use of R statistical software is required to support statistics learning.
Learning Approaches
Indicative Workload
The total volume of learning in MZB103 is 75 hours, consistent with the Australian Qualifications Framework, which describes the typical time required for a student with no prior achievement of the unit learning outcomes to develop the required knowledge and skills. An indicative breakdown of this workload is provided below.
Timetabled contact - 19 hours
- Lectures: 10 hours (2 hours x 5 weeks)
- Workshops: 5 hours (1 hour x 5 weeks)
- Assessment support: 4 hours (2 hours x 2 weeks)
Assessment - 28.5 hours
- Formative quizzes: 5 hours (1 hour x 5 weeks)
- Statistics portfolio contributions: 12 hours (3 hours x 4 weeks)
- Exam preparation: 10 hours (2 hours x 5 weeks)
- Invigilated exam: 1.5 hours
Self-directed learning - 27.5 hours
- Independent study across Weeks 1-5 (5.5 hours x 5 weeks), including:
- Pre-workshop preparation (2 hours x 5 weeks, potentially collaborative)
- Independent practice and consolidation
- Reading and review of lecture materials
Learning Design
This unit uses a structured, scaffolded "I do -- we do -- you do" model of learning. Core concepts and methods are first introduced and demonstrated by the lecturer in weekly lectures ("I do"). Students then apply these concepts in small-group workshops through guided activities, discussion, and peer explanation, with the teacher facilitating learning and providing support ("we do"). Students subsequently apply the same concepts independently in assessment tasks ("you do").
Lectures are delivered as 2-hour face-to-face interactive sessions, focussing on explanation of core concepts and demonstration of worked examples that model expected approaches.
Workshops are 1-hour face-to-face small-group classes designed to support active engagement and participation. In Week 1, workshops are teacher-led and introduce the workshop structure, expectations for participation, and the use of R statistical software in Google Colab. In Weeks 2-5, workshops incorporate structured student-led explanation of assigned activities, with the teacher guiding discussion, providing clarification, and addressing misconceptions as required.
Learning is further supported through self-directed study, including pre-workshop preparation, independent practice, and review of lecture materials. Students are encouraged to engage collaboratively with peers during preparation and workshops, while recognising that assessment tasks require individual demonstration of learning.
Optional assessment support sessions are scheduled during the teaching period to provide targeted assistance, supporting students' learning and preparation for assessment.
In addition, FREE STEM Support for Science Students workshops are also available throughout the year.
Feedback on Learning and Assessment
Feedback is provided throughout the unit to support students' learning, development of statistical understanding, and preparation for assessment.
Students receive ongoing formative feedback during face-to-face workshop classes, where the teacher provides guidance, clarification, and feedback on students' approaches as they work through activities. Workshops also incorporate collaborative discussion and peer explanation, enabling students to receive immediate informal feedback through questioning, comparison of approaches, and shared problem-solving.
Additional feedback is provided through weekly formative quizzes, which offer timely feedback to support self-assessment and help students identify areas requiring further practice. Feedback is also provided on portfolio assessment submissions, supporting students' understanding of assessment expectations and areas for improvement.
Portfolio assessment is structured to support learning over time. Portfolio Part 2 requires students to reflect on feedback and marks received for Portfolio Part 1, encouraging students to evaluate their learning and demonstrate development across the teaching period. Marks awarded for summative assessment items provide further feedback on students' achievement of the unit learning outcomes.
Exam feedback is available during the review of grade period by appointment with the unit coordinator.
An online communication channel will be available for you to engage with your peers and teaching staff. Teaching staff will review the channel at least once per week to confirm or correct information, respond to outstanding questions, and provide guidance on common issues arising in the unit. Participation in the channel provides opportunities to receive and give feedback through posting questions, responding to peers, and reviewing posts.
Assessment
Overview
The assessment items in this unit are designed to evaluate your level of achievement of the unit learning outcomes while supporting the progressive development of foundational statistical knowledge and skills through a range of assessment activities.
Weekly Online Quiz (Formative Only)
Weight: 0%
Individual or Group: Individual
Unit Learning Outcomes: ULO1
Formative weekly online multiple choice quizzes with automated feedback will be available, in addition to the summative portfolio assessment, to support your learning. Unlimited attempts are permitted. These quizzes are designed to allow you to self-assess your understanding of foundational statistics concepts. The quiz questions are exemplars for the multiple choice questions on the examination. This is formative-only assessment, the marks do not contribute to your final grade and do not need to be submitted.
Authentication of Learning
To support assessment integrity and individual accountability, students may be required to complete an authentication of learning process, including (but not limited to) cases where a student did not participate in the associated workshop, or where there is inconsistency between performance in portfolio assessment, the exam, or demonstrated understanding during workshop activities.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Statistics Portfolio
The portfolio assesses content from Weeks 1-5 and is submitted in two parts to support progressive learning, timely feedback, and consolidation of knowledge.
Portfolio Part 1 (due Week 3) covers content from Weeks 1-2, allowing students to receive feedback early in the teaching period on their understanding and application of foundational statistical concepts and methods.
Portfolio Part 2 (due Week 5) covers content from Weeks 3-5 and requires students to apply feedback from Portfolio Part 1, supporting reflection, consolidation, and improvement over time in preparation for the examination. The Week 5 component is a brief reflective task linked to the Week 5 workshop and is designed to be completed largely within workshop time, rather than adding substantial additional assessment workload in the week the assessment is due.
Each portfolio submission includes tasks directly related to the associated weekly workshop activities. Each submission also includes a brief reflective component linked to the relevant workshop.
The portfolio questions form exemplars for the short answer questions on the examination.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
The examination will cover all content (content from Weeks 1-5), and assess understanding, application and interpretation of foundational statistics concepts, techniques and models to solve scientific problems.
The examination will consist of 2 sections:
- Section A: Multiple Choice
- Section B: Short Answer
The formative online quizzes form the exemplars for Section A. The statistics portfolio and workshop class worksheets form exemplars for Section B.
You will not be permitted to take a resource sheet into the examination, the examination paper will instead contain a formula sheet, which is the same formula sheet provided to you at the beginning of the teaching period. You are encouraged to use and familiarise yourself with the formula sheet during the teaching period, prior to your examination. You will be permitted to use a calculator of any type.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
The examination will require attendance on QUT campus. The late submission period does not apply, and no extensions are available. if you can’t attend this exam due to special circumstances, you may apply to sit a deferred exam.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Requirements to Study
Costs
There are no out of the ordinary costs associated with the study of this unit.
Resources
Required readings for MZB103 will consist of lecture materials provided on Canvas. Recommended readings will also be provided from the recommended text listed below.
There are many reference texts for this unit, many of which can be located in the library. There are also many online resources such as lecture notes and some e-books that can be found online. Example reference texts are listed below.
All statistical computation will be performed with R statistical software inside the RStudio integrated development environment. Both of these pieces of software are required, are available in QUT computer labs and are available online at no charge for students to install on their personal computers.
Resource Materials
Recommended text(s)
Currell, G & Downman, A (2009) Essential Mathematics and Statistics for Science (2nd ed.) Wiley-Blackwell, Oxford.
Available to view online through QUT Library.
Reference book(s)
Ekstrom, C (2019) R Primer (2nd ed.) Taylor and Francis, Boca Raton, Florida.
Available to view online through QUT Library.
Software
R Statistical Software download for free
RStudio Integrated Development Environment (IDE) download for free
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit, as all classes will be held in ordinary lecture theatres and computer laboratories. Emergency exits and assembly areas will be pointed out in the first few lectures. You are referred to the University policy on health and safety.
http://www.mopp.qut.edu.au/A/A_09_01.jsp
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.ST01 Bachelor of Science
- Develop a broad, multidisciplinary understanding of science and a specialised, in-depth knowledge of at least one discipline.
Relates to: ULO1, ULO2, Statistics Portfolio, Examination (invigilated) - Use higher order thinking skills to design, plan, and conduct investigations and evaluate data to address scientific questions and challenges.
Relates to: ULO2, Statistics Portfolio, Examination (invigilated) - Develop and demonstrate key competencies in scientific practices and relevant technologies.
Relates to: ULO2, Statistics Portfolio, Examination (invigilated) - Communicate scientific findings, concepts and evidence-based reasoning to diverse audiences using a variety of methods.
Relates to: ULO3, Statistics Portfolio
SV02 Bachelor of Science
- Develop a broad, multidisciplinary understanding of science and a specialised, in-depth knowledge of at least one discipline.
Relates to: ULO1, ULO2, Statistics Portfolio, Examination (invigilated) - Use higher order thinking skills to design, plan, and conduct investigations and evaluate data to address scientific questions and challenges.
Relates to: ULO2, Statistics Portfolio, Examination (invigilated) - Develop and demonstrate key competencies in scientific practices and relevant technologies.
Relates to: ULO2, Statistics Portfolio, Examination (invigilated) - Communicate scientific findings, concepts and evidence-based reasoning to diverse audiences using a variety of methods.
Relates to: ULO3, Statistics Portfolio