This course has been adjusted for remote participation in Sem 2 2021, however students are encouraged to attend on-campus activities if possible.
Science is fundamentally about testing ideas, not collecting facts. But how do we test ideas? We do so by analysing numbers to reveal patterns, and then by designing experiments to exclude competing ideas that might explain what causes these patterns. In this course, you will discover how to design experiments and analyse data. You’ll then be equipped to critically assess scientific claims that you hear in the media. Think about how much of the news is about alleged medical breakthroughs, causes of health problems, and claims that one economic policy is better than another. We want to help you to sift out fake news, identify misleading ways to describe data and allow you to assess the importance of different factors in explaining the patterns we see in the world. This is a core life skill. And, naturally, if you take this course you’ll be better equipped to take the path to becoming a biologist yourself.
This course will explore the ways biologists design experiments, generate data and assess evidence using a variety of statistical techniques. We will discuss the value of different scientific approaches, including hypothesis-driven experiments and exploration of large-scale data generation–such as genome and transcriptome sequencing projects. We’ll consider examples from microbial, animal and plant biology, and you will carry out exercises to develop and test hypotheses, and critically interpret the results. You will have the opportunity to participate in all stages of a biological experiment. This will include the conception and design of a study, laboratory work, analysis of the data, drawing conclusions, writing the report and engaging graphical ways to present your findings. The R programming language will be taught and used throughout the course. Students will be required to use their laptop computers.
Note: Graduate students attend joint classes with undergraduates but are assessed separately.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Methodically apply different experimental approaches in biology with deep understanding on how to use experimental models.
- Proficiently apply R programming skills and be able to identify, choose and apply proper R packages to address biological problems.
- Design biological experiments to address critical questions in biology.
- Demonstrate technical proficiency in the use of appropriate analysis, and interpretation of qualitative and quantitative data.
- Apply, and critically assess the results of relevant statistical techniques in the context of the analysis of biological measurements.
- Integrate and apply the knowledge gained from specific research projects to problem solving in other areas of biology.
Other Information
Please email rsb.studentadmin@anu.edu.au to request a permission code to enrol in this course.
Indicative Assessment
- 3 x Assignments (30) [LO 1,2,3,4,5,6]
- Lab report (20) [LO 1,2,3,4,5]
- Mid-term quiz (10) [LO 1,2,4,5]
- PG Writing Assignment (10) [LO 1,3,4,5,6]
- Final Exam (30) [LO 1,3,4,5,6]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
The expected workload will consist of approximately 130 hours throughout the semester including:
- Face-to face component which may consist of approx. 2 x 1-hour lectures per week plus 12 x 3-hour workshops/tutorials/practicals throughout the semester. Together there will be approx. 60 hours of in-class component per semester.
- Approximately 70 hours of self directed study which will include preparation for lectures and workshops/tutorials and labs and other assessment tasks including written assignments, quizzes, and an exam.
Students are expected to actively participate and contribute towards discussions.
Inherent Requirements
To be determined
Requisite and Incompatibility
You will need to contact the Biology Teaching and Learning Centre to request a permission code to enrol in this course.
Prescribed Texts
Reading materials will be provided throughout the course. The primary textbook will be Whitlock and Schluter, The Analysis of Biological Data, edition 2 or 3.
Assumed Knowledge
Bachelor degree with first year biology; some knowledge of statistics.
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2021 | $4110 |
- International fee paying students
| Year | Fee |
|---|---|
| 2021 | $5880 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 6350 | 26 Jul 2021 | 02 Aug 2021 | 14 Sep 2021 | 29 Oct 2021 | In Person | View |
