Course coordinator
Associate Professor Katrina McGuigan
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- The Environment School
Introduces students to the process of biological research in fields ranging from ecology to genetics. In addition to lectures students will get hands-on experience including generating ideas and hypotheses, through to designing experiments, analysing real data-sets, critiquing published studies and communicating results.
Our ability to gather large data-sets in the laboratory and field is rapidly expanding, making it essential for modern practicing scientists to be able to organise, explain and use these data to make important scientific advances. The variability of organisms' (including humans!) responses to experimental treatments and natural conditions, which themselves are highly variable, makes both experimental design and statistical analysis essential skills for addressing research questions across all biological and environmental fields. This course provides students with the in-depth knowledge and tools to enable them to generate hypotheses and ﾠdesign robust experiments andﾠ field studies to test hypotheses or distinguish between competing hypotheses.ﾠ
The ability to critique methods, correctly analyse experimental or observational data andﾠinterpret and present the results will beﾠof greatﾠbenefit to students taking a wide range ofﾠresearch- and project-based courses, and in your future careers.
Course requirements
Assumed background
Basic statistics, e.g. STAT1201 and basic mathematical skills such as the manipulation of algebraic equations, logs, indices etc.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
STAT1201 or STAT1301
Incompatible
You can't enrol in this course if you've already completed the following:
BIOL2106
Course contact
Course staff
Lecturer
Associate Professor Katrina McGuigan
Professor Anthony Richardson
Dr Simon Hart
Ms Ella Cathcart-Van Weeren
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please attend only the Workshop and the Practical class to which you are allocated.
Aims and outcomes
To provide students with the skills to design, carry out and present creative, scientifically rigorous biological research projects, skills that will also enable them to critically evaluate other research projects.
To provide students with an ability to implement statistical tests using the programming language R, and to interpret R outputs in a meaningful way.ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Quantify uncertainty and make statistical statements about nature
LO2.
Construct, test and evaluate scientific hypotheses
LO3.
Critically evaluate the use of statistics in research
LO4.
Design experiments and observational studies to rigorous scientific standards
LO5.
Present data, analyses, hypotheses and results
LO6.
Use R as a tool to perform statistical analyses
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz |
Weekly Quiz
|
30% |
Quiz 1: 9/03/2026 - 13/03/2026 Quiz 2: 16/03/2026 - 20/03/2026 Quiz 3: 23/03/2026 - 27/03/2026 Quiz 4: 13/04/2026 - 17/04/2026 Quiz 5: 20/04/2026 - 24/04/2026 Quiz 6: 27/04/2026 - 1/05/2026 Quiz 7: 11/05/2026 - 15/05/2026 Quiz 8: 18/05/2026 - 22/05/2026 Quiz 9: 25/05/2026 - 29/05/2026
QUIZZES ARE IN CLASS DURING YOUR TIMETABLED PBL (WORKSHOP) SESSION IN EACH OF THE WEEKS INDICATED |
| Paper/ Report/ Annotation |
Analysis of Data & Presentation of Results
|
25% |
29/04/2026 2:00 pm |
| Examination |
End of semester exam
|
45% |
End of Semester Exam Period 6/06/2026 - 20/06/2026 |
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.
Assessment details
Weekly Quiz
- Identity Verified
- In-person
- Mode
- Written
- Category
- Quiz
- Weight
- 30%
- Due date
Quiz 1: 9/03/2026 - 13/03/2026
Quiz 2: 16/03/2026 - 20/03/2026
Quiz 3: 23/03/2026 - 27/03/2026
Quiz 4: 13/04/2026 - 17/04/2026
Quiz 5: 20/04/2026 - 24/04/2026
Quiz 6: 27/04/2026 - 1/05/2026
Quiz 7: 11/05/2026 - 15/05/2026
Quiz 8: 18/05/2026 - 22/05/2026
Quiz 9: 25/05/2026 - 29/05/2026
QUIZZES ARE IN CLASS DURING YOUR TIMETABLED PBL (WORKSHOP) SESSION IN EACH OF THE WEEKS INDICATED
- Other conditions
- Student specific, Time limited, Secure.
Task description
During your Problem Based Learning (PBL) class you will complete a short quiz. Quizzes are taken under exam conditions, in class (i.e., you must attend class to take the quiz).
The course is divided into 3 modules, 1 for each of your lecturers:
- Weeks 2, 4 & 5 [Anthony Richardson]: Quiz in weeks 3, 4 & 5
- Weeks 6, 7, 8 & 9 [Simon Hart]: Quiz in weeks 7, 8 & 9
- Weeks 10, 11, 12 & 13 [Katrina McGuigan]: Quiz in weeks 11, 12 & 13
That 30% is allocated equally to the 3 modules (10% for each)
In each Module, you will sit 3 quizzes (9 in total) - one per week excluding the 1st week of the module.
To calculate your overall grade for these PBL quizzes, we will take your best 2 of the 3 quizzes for each module.
Quizzes cannot be deferred - if you are unable to attend in a week, we will take the marks from the other two quizzes of the module.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Extensions are not available for this assessment as they are paced to match course content weekly. To calculate your overall grade for these PBL quizzes, we will take your best 2 of the 3 quizzes for each module. If you are unable to attend in a week, we will take the marks from the other two quizzes of the module.
Late submission
Exams submitted after the end of the submission time will incur a late penalty.
Analysis of Data & Presentation of Results
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 25%
- Due date
29/04/2026 2:00 pm
- Other conditions
- Student specific, Time limited.
Task description
Utilising knowledge and skills you have gained in Lectures, PBLs and Pracs, you will undertake data analysis in R, and submit a written report presenting the results.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Online submission by Turnitin only by the due date and time. Refer to Blackboard for the submission link. No hard copy or assignment cover sheets are required. Submission via email is not accepted.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
A maximum of 14 days extension may be permitted as feedback is released after 14 days & review of the assessment is discussed in class. Timely release of the feedback from this assessment is important as future learning material expands on ideas presented in this assessment and may be featured in the final exam.
Late submission
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.
You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item, then 10% of the maximum possible mark for the assessment item (assessment ‘marked from’ value) will be deducted as a late penalty for every day (or part day) late after the due date. For example, if you submit your assignment 1 hour late, you will be penalised 10%; if your assignment is 24.5 hours late, you will be penalised 20% (because it is late by one 24-hour period plus part of another 24-hour period).
End of semester exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 45%
- Due date
End of Semester Exam Period
6/06/2026 - 20/06/2026
- Other conditions
- Student specific, Time limited, Secure.
Task description
The exam will consist of questions covering all content taught throughout the whole semester (Weeks 2 - 13, inclusive). You will have two hours (120 minutes) writing time .
This will be a paper-based in person exam during the exam period. You will not be required to use R or write code for R but may be asked to interpret R output.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
See Additional Course Grading Information for the hurdle information relating to this assessment item.Exam details
| 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 |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 0% |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 30% |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: The minimum percentage required for this grade is: 45% |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 50% |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 65% |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 75% |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: The minimum percentage required for this grade is: 85% |
Additional course grading information
Assessment Hurdle
In order to pass this course, you must meet the following requirements (if you do not meet these requirements, the maximum grade you will receive will be a 3):
You must obtain 45% or more on the End of Semester Exam
Supplementary assessment
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 the link above 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 can take any form (such as a written report, oral presentation, examination or other appropriate assessment) and may test specific learning outcomes tailored to the individual student, or all learning outcomes.
To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment.
Additional assessment information
Applications for Extensions to Assessment Due Dates
Read the information contained in the following links carefully before submitting an application for extension to assessment due date.
For guidance on applying for an extension, information is available here: https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension
For the policy relating to extensions, information is available here (Part D): https://policies.uq.edu.au/document/view-current.php?id=184
Please note the University's requirements for medical certificates here: https://my.uq.edu.au/information-and-services/manage-my-program/uq-policies-and-rules/requirements-medical-certificates
Learning resources
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.
Library resources
Library resources are available on the UQ Library website.
Additional learning resources information
The course learning resources web site will contain lecture slides, activity sheets, the practical manual (and associated files), assignment details, and other resource materials.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (23 Feb - 01 Mar) |
Lecture |
Introduction to the Course - Katrina McGuigan Learn what to expect in the course - and how to set yourself up for success |
Problem-based learning |
Introduction to PBL sessions - Katrina McGuigan Refresh your statistical knowledge and find out more about how you will learn in this course |
|
Practical |
Getting Started with R In this session, you will set yourself up for a successful semester of learning with an R project. Learn how to write, save and execute code in RStudio. |
|
Multiple weeks From Week 2 To Week 5 |
Lecture |
Statistical Foundations - Anthony Richardson In this Foundations module, you will refamiliarise yourself with data and statistical principals, and begin to hone your expertise in using R to explore and present data. You will be introduced to the powerful, general tool for modeling data and hypothesis testing - the linear model. You will continue to build your understanding of linear models in the next module Each week - in Lecture, PBL and Prac - you will focus on particular statistical principals, and continue to build each week on your knowledge gained in earlier weeks. |
Problem-based learning |
Statistical Foundations - Anthony Richardson In this Foundations module, you will refamiliarise yourself with data and statistical principals, and begin to hone your expertise in using R to explore and present data. You will be introduced to the powerful, general tool for modeling data and hypothesis testing - the linear model. You will continue to build your understanding of linear models in the next module Each week - in Lecture, PBL and Prac - you will focus on particular statistical principals, and continue to build each week on your knowledge gained in earlier weeks. |
|
Practical |
Statistical Foundations - Anthony Richardson In this Foundations module, you will refamiliarise yourself with data and statistical principals, and begin to hone your expertise in using R to explore and present data. You will be introduced to the powerful, general tool for modeling data and hypothesis testing - the linear model. You will continue to build your understanding of linear models in the next module Each week - in Lecture, PBL and Prac - you will focus on particular statistical principals, and continue to build each week on your knowledge gained in earlier weeks. |
|
Multiple weeks From Week 6 To Week 9 |
Lecture |
Statistical Analysis and Experimental Designs - Simon Hart Having developed your expertise in `statistical thinking` in the first weeks of the course, you will now build on that to further develop your expertise in experimental design and data analysis. You will learn how to apply the linear model framework to different types of research questions and different types of data. Focusing on situations with a single continuously distributed response variable of interest, you will learn how to fit and interpret models with different types of predictor variables. You will again focus on one topic per week - in Lecture, PBL and Prac Week 6 – Multiple Regression (multiple continuous predictors) Week 7 – Comparing Groups (or Treatments) – Analysis of Variance (categorical predictors) Week 8 – Analysis of Covariance (continuous AND categorical predictors) Week 9 – Advanced linear models |
Problem-based learning |
Statistical Analysis and Experimental Designs - Simon Hart Week 6 – Multiple Regression (multiple continuous predictors) Week 7 – Comparing Groups (or Treatments) – Analysis of Variance (categorical predictors) Week 8 – Analysis of Covariance (continuous AND categorical predictors) Week 9 – Advanced linear models |
|
Practical |
Statistical Analysis and Experimental Designs - Simon Hart Week 6 – Multiple Regression (multiple continuous predictors) Week 7 – Comparing Groups (or Treatments) – Analysis of Variance (categorical predictors) Week 8 – Analysis of Covariance (continuous AND categorical predictors) Week 9 – Advanced linear models |
|
Mid-sem break (06 Apr - 12 Apr) |
No student involvement (Breaks, information) |
Mid Semester Break |
Multiple weeks From Week 10 To Week 13 |
Lecture |
Big data and machine learning - Katrina McGuigan In the final part of the course, we will build your expertise in interpreting shared information among variables. We will deepen your understanding of how complex patterns of association among variables can lead to outcomes from statistical analyses that are not obvious in simple inspections of data and summary statistics, and we introduce you to a widely used unsupervised machine learning approach for summarising big data (many variables). You will again focus on one topic per week - in Lecture, PBL and Prac
Week 10 – Re-visiting data summaries – variances, covariances and correlations NB: Lecture will be delivered in Prac timeslot due to public holiday Week 11 – Multiple Regression - Reloaded Week 12 – Principal Components Analysis Week 13 – Multivariate Analysis of Variance |
Problem-based learning |
Big data and machine learning - Katrina McGuigan Week 10 – No PBL this week due to public holiday Week 11 – Multiple Regression - Reloaded Week 12 – Principal Components Analysis Week 13 – Multivariate Analysis of Variance |
|
Practical |
Big data and machine learning - Katrina McGuigan Week 10 – Re-visiting Estimation – variances, covariances and correlations Week 11 – Principal Components Analysis Week 12 – Multiple Regression - Reloaded Week 13 – Multivariate Analysis of Variance |
Policies and procedures
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:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
- AI for Assessment Guide
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.