Course coordinator
See "Course Staff" on Blackboard for consultation times.
Analysis of scientific data and experiments: Design of experiments and ethical research. Data modelling and management. Exploratory data analysis. Randomness and probability. Statistical analysis including linear regression, analysis of variance, logistic regression, categorical data analysis, and non-parametric methods.
The aim of STAT1201 is to provide an understanding of the nature of scientific data and the subsequent need for statistical analysis. You will develop your statistical expertise and critical judgement in scientific studies, including an awareness of ethical issues in research and analysis. You will learn about the different types of data and how each can be visualised and summarised, and how you can make conclusions and predictions from the statistical analysis. You will also see that these statistical tools are based on simple mathematical ideas and associated assumptions.
In the School of Mathematics and Physics we are committed to creating an inclusive and empowering learning environment for all students. We value and respect the diverse range of experiences our students bring to their education, and we believe that this diversity is crucial for fostering a rich culture of knowledge sharing and meaningful exploration. We hold both students and staff accountable for actively contributing to the establishment of a respectful and supportive learning environment.
Bullying, harassment, and discrimination in any form are strictly against our principles and against ᅠUQ Policy,ᅠ and will not be tolerated. We have developed a ᅠsuite of resources ᅠto assist you in recognising, reporting, and addressing such behaviour. If you have any concerns about your experience in this course, we encourage you to tell a member of the course teaching team, or alternatively contact an SMP Classroom Inclusivity Champion (see Blackboard for contact details). Our Inclusivity Champions are here to listen, to understand your concerns, and to explore potential actions that can be taken to resolve them. Your well-being and a positive learning atmosphere are of utmost importance to us.
Mathematics equivalent to Queensland Mathematical Methods.
You'll need to complete the following courses before enrolling in this one:
MATH1040; or a grade of C or higher in Queensland Year 12 Mathematical Methods (Units 3 & 4) (or equivalent).
You can't enrol in this course if you've already completed the following:
ECON1310, ENVM2000, STAT1301, STAT2201, STAT2203, STAT2701, PUBH2007, HRSS3100, STAT7120.
Not available to BE, BE/Biotech, BE/BSc students, BMath, BMath/BArts, BMath/BCom, BMath/BEcon, BMath/BEd(Sec), BMath/BSc
See "Course Staff" on Blackboard for consultation times.
The timetable for this course is available on the UQ Public Timetable.
All classes will be conducted on campus. Consult your personal timetable for times and locations. Students are expected to attend these sessions in person unless they have a valid reason for being unable to attend (such as illness).
Important: if you are ill, then do not attend any classes in person. Alternative arrangements can be organised – consult Blackboard for details.
Lectures start in Week 1.
Applied classes start in Week 2. There are no applied classes in Weeks 1, 3 or 10.
The aim of STAT1201 is to provide an understanding of the nature of scientific data and the subsequent need for statistical analysis. You will develop your statistical expertise and critical judgement in scientific studies, including an awareness of ethical issues in research and analysis. You will learn about the different types of data and how each can be visualized and summarized, and how you can make conclusions and predictions from the statistical analysis. You will also see that these statistical tools are based on simple mathematical ideas and associated assumptions.
After successfully completing this course you should be able to:
LO1.
Explain the nature of scientific data and the need for statistical analysis.
LO2.
Identify factors related to the design of a scientific study, including sample size and power.
LO3.
Identify and critically evaluate the role of data analysis and statistics in scientific research and publications.
LO4.
Identify and critically evaluate ethical issues in scientific research.
LO5.
Demonstrate a foundational knowledge of statistical methods by being able to carry out simple statistical procedures by hand.
LO6.
Use statistical software appropriately and confidently for exploratory data analysis and to make relevant statistical conclusions.
LO7.
Plan and carry out a research project, and analyse and communicate the results.
Category | Assessment task | Weight | Due date |
---|---|---|---|
Participation/ Student contribution, Quiz, Tutorial/ Problem Set | Online Quizzes | 16% |
Quiz 1 8/08/2025 4:00 pm Quiz 2 22/08/2025 4:00 pm Quiz 3 29/08/2025 4:00 pm Quiz 4 5/09/2025 4:00 pm Quiz 5 12/09/2025 4:00 pm Quiz 6 19/09/2025 4:00 pm Quiz 7 26/09/2025 4:00 pm Quiz 8 17/10/2025 4:00 pm Quiz 9 24/10/2025 4:00 pm Quiz 10 31/10/2025 4:00 pm
Individual questions are due by 4:00 pm Fridays. Collaborative questions are due by the end of your allocated applied class. |
Essay/ Critique | Paper Review | 14% |
29/08/2025 4:00 pm |
Presentation, Project | Research Project | 20% |
24/10/2025 4:00 pm |
Examination |
Final Examination
|
50% |
End of Semester Exam Period 8/11/2025 - 22/11/2025 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Quiz 1 8/08/2025 4:00 pm
Quiz 2 22/08/2025 4:00 pm
Quiz 3 29/08/2025 4:00 pm
Quiz 4 5/09/2025 4:00 pm
Quiz 5 12/09/2025 4:00 pm
Quiz 6 19/09/2025 4:00 pm
Quiz 7 26/09/2025 4:00 pm
Quiz 8 17/10/2025 4:00 pm
Quiz 9 24/10/2025 4:00 pm
Quiz 10 31/10/2025 4:00 pm
Individual questions are due by 4:00 pm Fridays. Collaborative questions are due by the end of your allocated applied class.
In each of the ten weeks that there is an applied classes there will be a quiz to complete online. Each quiz will involve 10 questions:
Your best eight quiz marks will each count 2% towards your overall grade (for a maximum of 16%).
Complete via Blackboard.
You may be able to apply for an extension.
An approved extension will be applied to the individual questions but taken as an exemption from the collaborative questions in the applied class, with the individual questions re-weighted to be worth 2% instead.
If you fail to meet the submission deadline for the individual questions you will not receive any marks unless an extension has been approved. If you are unable to complete the group quiz questions in your applied class then you are welcome to attend a different applied class during the week instead (space permitting).
29/08/2025 4:00 pm
The Paper Review involves finding a scientific paper of interest to you through the library. You can choose any paper you like as long as it has a P-value and a DOI, and was published no earlier than 2022. Each paper can only be reviewed by one group so once you have your paper you must register it on Blackboard.
You will provide a brief summary of the use of statistical inference in the paper you choose and an essay discussing the ethical issues involved in the study. See Blackboard for example reviews.
The review is submitted using a form on Blackboard. The combined word length of the statistical component should not exceed 250 words, while the ethics essay has a maximum of 500 words.
The Paper Review may be completed in groups of up to three students. Use the tools on Blackboard if you want to form a group. Groups must be formed by the due date.
Complete via Blackboard
You may be able to apply for an extension.
See ADDITIONAL ASSESSMENT INFORMATION for the extension and deferred examination information relating to this assessment item.
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
The late submission penalty will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24 hour block is recorded from the time the submission is due.
24/10/2025 4:00 pm
For the Research Project you will design a study, carry it out with a population of virtual subjects to collect your data, and then analyse the results and present them in the form of a presentation, as would be given at a scientific conference.
The research project may be completed in groups of up to three students.
Your video should be roughly 6 minutes in length and feature each person in the group talking to camera (separately or together). Further information about creating your video is provided on Blackboard.
Submit via Blackboard.
You may be able to apply for an extension.
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
The late submission penalty will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24 hour block is recorded from the time the submission is due.
End of Semester Exam Period
8/11/2025 - 22/11/2025
The final examination in this course will be held during the end-of-semester examination period. It will be an in-person exam held on campus.
The final examination will cover the statistics and ethics content of the course, as covered in UQ Extend, lectures and applied classes.
You will be provided with a sheet of useful definitions and formulas.
Planning time | 10 minutes |
---|---|
Duration | 120 minutes |
Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
Open/closed book | Closed book examination - no written materials permitted |
Exam platform | Paper based |
Invigilation | Invigilated in person |
You may be able to defer this exam.
See ADDITIONAL ASSESSMENT INFORMATION for the extension and deferred examination information relating to this assessment item.
Full criteria for each grade is available in the Assessment Procedure.
Grade | Cut off Percent | Description |
---|---|---|
1 (Low Fail) | 0 - |
Absence of evidence of achievement of course learning outcomes. Course grade description: You will be able to partially analyse very few data settings. Presentation of statistical results will be poor and accuracy in graphical and numerical work will be low. |
2 (Fail) | 20 - |
Minimal evidence of achievement of course learning outcomes. Course grade description: You will be able to partially analyse a few important data settings. Presentation of statistical results may be poor and accuracy in graphical and numerical work may be low. |
3 (Marginal Fail) | 45 - |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: You will be able to partially analyse the important data settings. You will demonstrate the ability to present statistical results and show some accuracy in graphical and numerical work. |
4 (Pass) | 50 - |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: You will be able to analyse the important data settings, identifying some key assumptions that might affect the analysis. You will demonstrate the ability to present statistical results and show accuracy in graphical and numerical work. |
5 (Credit) | 65 - |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: You will be able to analyse many data settings, identifying the key assumptions that might affect the analysis. You will demonstrate the ability to present statistical results and show accuracy in graphical and numerical work. |
6 (Distinction) | 75 - |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: You will be able to analyse most data settings, identifying important assumptions and other factors that might affect the analysis. You will demonstrate proficiency in communicating statistical ideas and a high level of accuracy in graphical and numerical work. |
7 (High Distinction) | 85 - |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: You will be able to analyse a broad range of data settings, providing insight and thoroughness in the form of necessary assumptions and other factors that might affect the analysis. You will demonstrate excellent proficiency in communicating statistical ideas and a high level of accuracy in graphical and numerical work |
If you receive less than 40% of the available marks on the Final Examination then the maximum grade you can obtain is 3.
Supplementary assessment is available for this course.
Should you fail a course with a grade of 3, you may be eligible for supplementary assessment. Refer to my.UQ for information on supplementary assessment and how to apply.
Supplementary assessment provides an additional opportunity to demonstrate you have achieved all the required learning outcomes for a course.
If you apply and are granted supplementary assessment, the type of supplementary assessment set will consider which learning outcome(s) have not been met.
Supplementary assessment in this course will be a 2-hour examination similar in style to the end-of-semester examination. To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment.
Group Work
In this course we do not teach you how to work in groups but you are welcome to complete the Paper Review and Research Project in a group of up to three students if you wish. Use the tool on Blackboard (under Assessment) if you want to form a group. After the Paper Reviewᅠ is due the groups will reset and the group formation tool for the Research Project will become available (so you will need to re-register your group, or form a new group if you want). Please keep in mind the following three rules:
Remarks for paper review
As the paper review includes two components, namely the statistics part and the ethics part, in their re-mark application students should specify whether their application is for either part only, or for both parts. Only the requested component/s will be considered for re-marking.
Artificial Intelligence
To pass this course, students will be required to demonstrate a detailed understanding of course material together with a range of associated skills independent of Artificial Intelligence (AI) and Machine Translation (MT) tools.
For assessment tasks that are completed in-person (including examinations) termed “secure assessment”, the use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted unless otherwise advised. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Other non-secure assessment tasks (such as assignments) are designed to help you develop your understanding and skills, and to prepare you for secure assessment. You are thus generally encouraged to complete such assessment without the use of AI/MT, unless explicitly advised to the contrary in the assessment item. In any event, if you choose to use such tools, then you must clearly reference any such use within your submitted work. A failure to reference AI or MT use may constitute student misconduct under the Student Code of Conduct.
Applications for Extensions to Assessment Due Dates
Extension requests are submitted online via my.UQ – applying for an extension. Extension requests received in any other way will not be approved. Additional details associated with extension requests, including acceptable and unacceptable reasons, may be found at my.UQ.
Please note:
Applications to defer an exam
In certain circumstances you can apply to take a deferred examination for in-semester and end-of-semester exams. You'll need to demonstrate through supporting documentation how unavoidable circumstances prevented you from sitting your exam. If you can’t, you can apply for a one-off discretionary deferred exam.
Deferred Exam requests are submitted online via mySi-net. Requests received in any other way will not be approved. Additional details associated with deferred examinations, including acceptable and unacceptable reasons may be found at my.UQ.
Please note:
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Find the required and recommended resources for this course on the UQ Library website.
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Learning period | Activity type | Topic |
---|---|---|
Week 1 (28 Jul - 03 Aug) |
Lecture |
UQ Extend - Module 1 Evidence from data Learning outcomes: L01, L02, L03 |
Week 2 (04 Aug - 10 Aug) |
Lecture |
UQ Extend - Module 2 Ethics in science Learning outcomes: L04 |
Applied Class |
Applied Class 1 Learning outcomes: L01, L02 |
|
Week 3 (11 Aug - 17 Aug) |
Lecture |
UQ Extend - Module 3 Exploring data Learning outcomes: L01, L05, L06 |
Week 4 (18 Aug - 24 Aug) |
Lecture |
UQ Extend - Module 4 Understanding randomness Learning outcomes: L05, L06 |
Applied Class |
Applied Class 2 Learning outcomes: L02, L03, L04 |
|
Week 5 (25 Aug - 31 Aug) |
Lecture |
UQ Extend - Module 5 Sampling distributions Learning outcomes: L05, L06 |
Applied Class |
Applied Class 3 Learning outcomes: L02, L03, L04, L05 |
|
Week 6 (01 Sep - 07 Sep) |
Lecture |
UQ Extend - Module 6 Statistical inference Learning outcomes: L02, L05, L06 |
Applied Class |
Applied Class 4 Learning outcomes: L01, L02, L06, L07 |
|
Week 7 (08 Sep - 14 Sep) |
Lecture |
UQ Extend - Module 7 Comparing means Learning outcomes: L02, L05, L06 |
Applied Class |
Applied Class 5 Learning outcomes: L01, L05, L06 |
|
Week 8 (15 Sep - 21 Sep) |
Lecture |
UQ Extend - Module 8 Regression models Learning outcomes: L02, L05, L06 |
Applied Class |
Applied Class 6 Learning outcomes: L01, L02, L06, L07 |
|
Week 9 (22 Sep - 28 Sep) |
Lecture |
UQ Extend - Module 9 Multiple predictors Learning outcomes: L01, L02, L05, L06 |
Applied Class |
Applied Class 7 Learning outcomes: L01, L05, L06 |
|
Week 10 (06 Oct - 12 Oct) |
Lecture |
UQ Extend - Module 10 Experimental design in practice Learning outcomes: L01, L02 |
Week 11 (13 Oct - 19 Oct) |
Lecture |
UQ Extend - Module 11 Categorical data analysis Learning outcomes: L05, L06 |
Applied Class |
Applied Class 8 Learning outcomes: L01, L02, L03, L05, L07 |
|
Week 12 (20 Oct - 26 Oct) |
Lecture |
UQ Extend - Module 12 Nonparametric methods Learning outcomes: L05, L06 |
Applied Class |
Applied Class 9 Learning outcomes: L05, L06 |
|
Week 13 (27 Oct - 02 Nov) |
Lecture |
Case Studies and Revision Learning outcomes: L01, L02, L03, L05, L06 |
Applied Class |
Applied Class 10 Learning outcomes: L05, L06 |
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.