Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- 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 andᅠdesign robust experiments andᅠfield studies to test hypotheses or distinguish between competing hypotheses.ᅠThe ability to critique methods, correctly analyse experimental or observational data andᅠinterpret and present the results will beᅠof greatᅠbenefit to studentsᅠtaking a wide range ofᅠresearch- and project-based courses.
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
Jointly taught details
This course is jointly-taught with:
All classes of BIOL2006 are shared with BIOL2106.
Course contact
Course staff
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please attend only the PBL (Problem-Based Learning) and the Prac class 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 (cancelled) 10/03/2025 - 14/03/2025 Quiz 2: 17/03/2025 - 21/03/2025 Quiz 3: 24/03/2025 - 28/03/2025 Quiz 4: 7/04/2025 - 11/04/2025 Quiz 5: 14/04/2025 - 18/04/2025 Quiz 6: 28/04/2025 - 2/05/2025 Quiz 7: 12/05/2025 - 16/05/2025 Quiz 8: 19/05/2025 - 23/05/2025 Quiz 9: 26/05/2025 - 30/05/2025
QUIZZES ARE IN CLASS DURING YOUR TIMETABLED PBL SESSION IN EACH OF THE WEEKS INDICATED |
Paper/ Report/ Annotation |
Analysis of Data & Presentation of Results
|
25% |
29/04/2025 2:00 pm |
Examination |
End of semester exam
|
45% |
End of Semester Exam Period 7/06/2025 - 21/06/2025 |
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.
Assessment details
Weekly Quiz
- Identity Verified
- In-person
- Mode
- Written
- Category
- Quiz
- Weight
- 30%
- Due date
Quiz 1 (cancelled) 10/03/2025 - 14/03/2025
Quiz 2: 17/03/2025 - 21/03/2025
Quiz 3: 24/03/2025 - 28/03/2025
Quiz 4: 7/04/2025 - 11/04/2025
Quiz 5: 14/04/2025 - 18/04/2025
Quiz 6: 28/04/2025 - 2/05/2025
Quiz 7: 12/05/2025 - 16/05/2025
Quiz 8: 19/05/2025 - 23/05/2025
Quiz 9: 26/05/2025 - 30/05/2025
QUIZZES ARE IN CLASS DURING YOUR TIMETABLED PBL SESSION IN EACH OF THE WEEKS INDICATED
- Other conditions
- Student specific, Time limited.
Task description
During your PBLs 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 [Daniel]: Quiz in weeks 4 & 5
Weeks 6, 7, 8 & 9 [Simon]: Quiz in weeks 7, 8 & 9
Weeks 10, 11, 12 & 13 [Katrina]: 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 modules 2 and 3. For module 1, due to the postponement of teaching in Week 3 to 4, there are Quizzes only in Week 4 and 5 and both these quizzes will contribute to module 1.
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 (Modules 2 and 3 only).
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.
Quizzes occur weekly and cannot be extended or deferred. To calculate your overall grade for these PBL quizzes, we will take your best 2 of the 3 quizzes for modules 2 and 3. For module 1, due to the postponement of teaching in Week 3 to 4, there are Quizzes only in Week 4 and 5 and both these quizzes will contribute to module 1.
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 (Modules 2 and 3 only).
Analysis of Data & Presentation of Results
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 25%
- Due date
29/04/2025 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. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
It is the responsibility of the student to ensure the on time, correct and complete submission of all assessment items.ᅠ
Please ensure you receive and save the submission confirmation for all submitted items, you may be asked to produce this as evidence of your submission.ᅠwritten assessment must be submitted via the appropriate Turnitin submission portal, which can be found within the Blackboard site. You are responsible for ensuring that your submission is complete. It is wise to re-enter the Turnitin portal and confirm that your submission is there and that it has not been altered during the submission process.ᅠ
By submitting work through Turnitin you are deemed to have accepted the following declaration “I certify that this assignment is my own work and has not been submitted, either previously or concurrently, in whole or in part, to this University or any other educational institution, for marking or assessment”.ᅠ
In the case of a Blackboard outage, please contact the Course Coordinator as soon as possible to confirm the outage with ITS.ᅠ
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. See the Additional assessment information section further below for information relating to extension and deferral applications.
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
7/06/2025 - 21/06/2025
- Other conditions
- Student specific, Time limited.
Task description
The exam will consist of questions covering all material delivered in all Lectures and all PBL sessions. You will have two hours (120 minutes) writing time .
This will be a paper-based face-to-face 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
In order to pass this course, you must obtain an overall mark of 45% or more in the end of semester exam.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 |
Materials | bilingual dictionary |
Exam platform | Paper based |
Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
See the Additional information section further below for information relating to extension and deferral applications.
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% |
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 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 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
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:
- Requests for an extension to an assessment due date must be submitted through your my.UQ portal and you must provide documentation of your circumstances, as soon as it becomes evident that an extension is needed. Your application must be submitted on or before the assessment item's due date and time.
- Applications for extension can take time to be processed so you should continue to work on your assessment item while awaiting a decision. We recommend that you submit any completed work by the due date, and this will be marked if your application is not approved. Should your application be approved, then you will be able to resubmit by the agreed revised due date.
- If an extension is approved, you will be notified via your my.UQ portal and the new date and time for submission provided. It is important that you check the revised date as it may differ from the date that you requested.
- If the basis of the application is a medical condition, applications should be accompanied by a medical certificate dated prior to the assignment due date. If you are unable to provide documentation to support your application by the due date and time you must still submit your application on time and attach a written statement (Word document) outlining why you cannot provide the documentation. You must then upload the documentation to the portal within 24 hours.
- If an extension is being sought on the basis of exceptional circumstances, it must be accompanied by supporting documentation (eg. Statutory declaration).
- For extensions based on a SAP you may be granted a maximum of 7 days (if no earlier maximum timeframe applies). See the Extension or Deferral availability section of each assessment for timeframes. Your SAP is all that is required as documentation to support your application. However, additional extension requests for the assessment item will require the submission of additional supporting documentation e.g., a medical certificate. All extension requests must be received by the assessment due date and time.
- An extension for an assessment item due within the teaching period in which the course is offered, must not exceed four weeks in total. If you are incapacitated for a period exceeding four weeks of the teaching period, you are advised to apply for Removal of Course.
- If you have been ill or unable to attend class for more than 4 weeks, you are advised to carefully consider whether you are capable of successfully completing your courses this semester. You might be eligible to withdraw without academic penalty - seek advice from the Faculty that administers your program.
- Students may be asked to submit evidence of work completed to date. Lack of adequate progress on your assessment item may result in an extension being denied.
- There are no provisions for exemption from an assessment item within UQ rules. If you are unable to submit an assessment piece then, under special circumstances, you may be granted an exemption, but may be required to submit alternative assessment to ensure all learning outcomes are met.
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:
- Applications can be submitted no later than 5 calendar days after the date of the original exam.
- There are no provisions to defer a deferred exam. You need to be available to sit your deferred examination.
- Your deferred examination request(s) must have a status of "submitted" in mySI-net to be assessed.
- All applications for deferred in-semester examinations are assessed by the relevant school. Applications for deferred end-of-semester examinations are assessed by the Academic Services Division.
- You’ll receive an email to your student email account when the status of your application is updated.
- If you have a medical condition, mental health condition or disability and require alternative arrangements for your deferred exam you’ll need to complete the online alternative exam arrangements through my.UQ. This is in addition to your deferred examinations request. You need to submit this request on the same day as your request for a deferred exam or supplementary assessment. Contact Student Services if you need assistance completing your alternative exam arrangements request.
Plagiarism:ᅠ
You should be aware that the University employs purpose built software to detect plagiarism. It is very important that you understand clearly the practical meaning of plagiarism.
Students are encouraged to read theᅠUQ Academic Integrity and Plagiarism policyᅠwhich makes a comprehensive statement about the University's approach to plagiarism, including the approved use of plagiarism detection software, the consequences of plagiarismᅠand the principles associated with preventing plagiarism.
DEFINITION OF PLAGIARISM: Plagiarism is the act of misrepresenting as one's own original work the ideas, interpretations, words or creative works of another. These include published and unpublished documents, designs, music, sounds, images, photographs, computer codes and ideas gained through working in a group. These ideas, interpretations, words or works may be found in print and/or electronic media.
EXAMPLES OF PLAGIARISM:
1. Direct copying of paragraphs, sentences, a single sentence or significant parts of a sentence;
2. Direct copying of paragraphs, sentences, a single sentence or significant parts of a sentence with an end reference but without quotation marks around the copied text;
3. Copying ideas, concepts, research results, computer codes, statistical tables, designs, images, sounds or text or any combination of these;
4. Paraphrasing, summarising or simply rearranging another person's words, ideas, etc without changing the basic structure and/or meaning of the text;
5. Offering an idea or interpretation hat is not one's own without identifying whose idea or interpretation it is;
6. A 'cut and paste' of statements from multiple sources;
7. Presenting as independent, work done in collaboration with others;
8. Copying or adapting another student's original work into a submitted assessment item.
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
Find the required and recommended resources for this course on the UQ Library website.
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 (24 Feb - 02 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 - Daniel Ortiz-Barrientos In this introductory module, you will learn about common parameters and statistics (Estimation) and train yourself in statistical thinking (hypothesis testing). We'll introduce you to the powerful, general tool for modeling data and hypothesis testing - the linear model - and we'll show you how to present your data and results in tables, figures and in text. You will focus on one topic per week - in Lecture, PBL and Prac Week 2 - Estimation Week 3 - Statistical Hypothesis Testing Week 4 - Introduction to Linear Models - simple linear regression Week 5 - Presentation of Data and Results |
Problem-based learning |
Statistical Foundations - Daniel Ortiz-Barrientos Week 2 - Estimation Week 3 - Statistical Hypothesis Testing Week 4 - Introduction to Linear Models Week 5 - Presentation of Data and Results |
|
Practical |
Statistical Foundations - Daniel Ortiz-Barrientos Week 2 - Estimation Week 3 - Statistical Hypothesis Testing Week 4 - Introduction to Linear Models Week 5 - Presentation of Data and Results |
|
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 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 (21 Apr - 27 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 introduce you to the widely used unsupervised machine learning approach for summarising big data (many variables), and 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. You will again focus on one topic per week - in Lecture, PBL and Prac
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 |
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 |
|
Problem-based learning |
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 - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.