• Class Number 6036
  • Term Code 3360
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Marcin Adamski
    • Prof Adrienne Nicotra
    • Dr Marcin Adamski
    • Dr Teresa Neeman
  • Class Dates
  • Class Start Date 24/07/2023
  • Class End Date 27/10/2023
  • Census Date 31/08/2023
  • Last Date to Enrol 31/07/2023
SELT Survey Results

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:

  1. Methodically apply different experimental approaches in biology with deep understanding on how to use experimental models.
  2. Proficiently apply R programming skills and be able to identify, choose and apply proper R packages to address biological problems.
  3. Design biological experiments to address critical questions in biology.
  4. Demonstrate technical proficiency in the use of appropriate analysis, and interpretation of qualitative and quantitative data.
  5. Apply, and critically assess the results of relevant statistical techniques in the context of the analysis of biological measurements.
  6. Integrate and apply the knowledge gained from specific research projects to problem solving in other areas of biology.

Research-Led Teaching

Students will design and perform an in-lab experiment as well as perform theoretical designs of several other experiments and analyze provided data. They will write reports on these activities. The data will be analysed using the techniques covered in lectures. Students will use the same analytical techniques normally used in the School research laboratories.

Additional Course Costs

Textbooks used in this course:

  • Introductory R by R.J. Knell (e-book)
  • The Analysis of Biological Data by M.C. Whitlock and D. Schluter (book, or e-book)

The textbooks be can purchased, but they are also available in ANU library.

Examination Material or equipment

Laptop computer with R and RStudio and internet access.

Required Resources

Modern laptop with internet and ANU VPN connectivity and RStudio installed. The laptop's battery should be good enough for at least 3 hours of work.

Recommended student system requirements 

ANU courses commonly use a number of online resources and activities including:

  • video material, similar to YouTube, for lectures and other instruction
  • two-way video conferencing for interactive learning
  • email and other messaging tools for communication
  • interactive web apps for formative and collaborative activities
  • print and photo/scan for handwritten work
  • home-based assessment.

To fully participate in ANU learning, students need:

  • A computer or laptop. Mobile devices may work well but in some situations a computer/laptop may be more appropriate.
  • Webcam
  • Speakers and a microphone (e.g. headset)
  • Reliable, stable internet connection. Broadband recommended. If using a mobile network or wi-fi then check performance is adequate.
  • Suitable location with minimal interruptions and adequate privacy for classes and assessments.
  • Printing, and photo/scanning equipment

For more information please see https://www.anu.edu.au/students/systems/recommended-student-system-requirements

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Other Information

The course materials, including lecture presentation slides and workshop's and practical manuals will be made available on the course Wattle site.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction to programming in R using RStudio. Setup of RStudio on students' laptops. Learning basics of R.
2 Introduction to probability and statistics. Statistical data presentation and analysis in R.
3 Statistical Tests: Chi-square/Fisher Exact and t-Test. Use of R in statistical data analysis with focus on statistical hypotheses testing. Hand-out of assignment 1.
4 Advanced experimental designs, ANOVA Continued learning of R with focus on linear regression and analysis of variance. Hand-in of assignment 1.
5 Experiments - from planning to conclusions on the example of photoreactivation in yeasts. The yeast UV-damage photoreactivation experiment: Design of the experiment, and analysis of the data. Hand-out of assignment 2.
6 Linear regression and non-parametric tests. Learning about more sophistacted experimental designs and analysis of multi-factor data. Hand-in of assignment 2, mid-semester quiz (Wattle).
7 Design and data analysis of experiments in Humans Journal-club style lectures and a workshop focused on experimental design approaches with focus on statistical power and size of the sample. Hand-out of assignment 3.
8 Experimental data analysis with ANOVA - the Seed of Design module - part 1. Designing of the seed germination experiment, the related in-lab practical. Hand-in of assignment 3, hand-out of the lab report.
9 Bayesian Inference in Biological Data Analysis - part 1. Bayesian approaches in biological data analysis - introduction. Recording of the germination data in the 'Seed of Design' experiment.
10 Bayesian Inference in Biological Data Analysis - part 2. Bayesian approaches in biological data analysis - introduction. Recording of the germination data in the 'Seed of Design' experiment. Recording of the germination data in the 'Seed of Design' experiment.
11 Experimental data analysis with ANOVA - the Seed of Design module - part 2. Data analysis, discussion and conclusions from the 'Seed of Design' experiment.
12 Statistical approaches in massive sequencing experiments with focus on gene expression data from RNA-Seq. Analysis of gene expression data from RNA-Seq experiment. Intensive use of R and R packages. Recording of the germination data in the 'Seed of Design' experiment. Hand-in of the lab report.

Tutorial Registration

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.

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 - Probability & Statistics 10 % 17/08/2023 28/08/2023 1, 2, 4, 5
Assignment 2 - Photoreactivation Experiment 10 % 31/08/2023 23/09/2022 1, 2, 3, 4, 5
Mid-semester Quiz 10 % 01/09/2023 01/09/2023 1, 2, 3, 4, 5
Assignment 3 - Data analysis in medical experiments 10 % 28/09/2023 03/10/2023 1, 2, 3, 4, 5, 6
Lab Report - Seed Germination Experiment 20 % 26/10/2023 07/11/2023 1, 2, 3, 4, 5, 6
PG Students Assignment - Essay 10 % 19/10/2022 30/10/2022 1, 2, 3, 4, 5, 6
Final exam 30 % * * 1, 2, 3, 4, 5, 6

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using 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. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.


Participation in lectures - although highly recommended - is not compulsory. Participation in the workshops and labs - in-class or remote - is compulsory. All lectures will be delivered on-campus, and the recordings will be available on the course Echo360 site. Workshops will be delivered in on-campus, and - at the same time - online on Zoom. The lab (Friday, semester week 8) will be delivered only on-campus, and will not be recorded. The remote students will be provided with description of the lab activities and ready-to-use data to analyze. All students will need to submit the lab report.


Please note, that where a date range is used in the Assessment Summary in relation to exams, the due date and return date indicate the approximate timeframe in which the exam will be held and results returned to the student (official end of Semester results released on ISIS). Students should consult the course wattle site and the ANU final examination timetable to confirm the date, time and venue of the exam.

Assessment Task 1

Value: 10 %
Due Date: 17/08/2023
Return of Assessment: 28/08/2023
Learning Outcomes: 1, 2, 4, 5

Assignment 1 - Probability & Statistics

Probability and Statistical problem sets. The delivery is an R Markdown document with R code and open text solutions to the tasks.

Assessment Task 2

Value: 10 %
Due Date: 31/08/2023
Return of Assessment: 23/09/2022
Learning Outcomes: 1, 2, 3, 4, 5

Assignment 2 - Photoreactivation Experiment

Determine the effects of photoreactivation on yeast UV-caused DNA damage. The delivery is a report stating the experiment objective and design, statistical data analysis and results, and the discussion and conclusions.

Assessment Task 3

Value: 10 %
Due Date: 01/09/2023
Return of Assessment: 01/09/2023
Learning Outcomes: 1, 2, 3, 4, 5

Mid-semester Quiz

In-classroom Wattle quiz with approximately 12 questions of equal value. The quiz questions will be related to the statistical and data analysis subjects and problems discussed in weeks 1 - 6.

Assessment Task 4

Value: 10 %
Due Date: 28/09/2023
Return of Assessment: 03/10/2023
Learning Outcomes: 1, 2, 3, 4, 5, 6

Assignment 3 - Data analysis in medical experiments

Perform critical analysis of given data from medical experiment. The delivery is an R Markdown document with R code and open text solutions to the tasks.

Assessment Task 5

Value: 20 %
Due Date: 26/10/2023
Return of Assessment: 07/11/2023
Learning Outcomes: 1, 2, 3, 4, 5, 6

Lab Report - Seed Germination Experiment

Use of replicated blocked experimental designs in seed germination of Australian native plants. The delivery is a scientific report stating the introduction, the materials and methods, the results, and the discussion sections. The report should include the experiment objectives and design, present and discuss the results and the conclusions. It should include all the relevant references.

Assessment Task 6

Value: 10 %
Due Date: 19/10/2022
Return of Assessment: 30/10/2022
Learning Outcomes: 1, 2, 3, 4, 5, 6

PG Students Assignment - Essay

Statistical design of experiments in practice. The delivery is an essay discussing aspects of statistical experiment design and practical data analysis.

Assessment Task 7

Value: 30 %
Learning Outcomes: 1, 2, 3, 4, 5, 6

Final exam

Final exam, 2 hours. The exam consists of two parts:

  1. Section A - Quiz - 12 questions of equal value - 45% of the exam mark
  2. Section B - R Assignment - 3 problems of equal value - 55% of the exam mark

The date range in the Assessment Summary indicates the start of the end of semester exam period and the date official end of semester results are released on ISIS. Please check the ANU final Examination Timetable http://www.anu.edu.au/students/program-administration/assessments-exams/examination-timetable to confirm the date, time and location exam.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.


The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

There are no hand-written assignments requiring hardcopy submission in this course.

Late Submission

Late submission permitted. Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item. Late submission is not accepted for take-home examinations.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Returning Assignments

All assignments will be marked and feedback will be provided on the course Wattle site in the relevant assignment or TurnitIn activities. A Wattle announcement will be made when marked assignments are available.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

No resubmission of assignments is allowed.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Dr Marcin Adamski
6125 2761

Research Interests

Genomic and high-throughput sequencing experiments; scientific computing; experimental designs in biology.

Dr Marcin Adamski

Monday 12:10 13:10
Monday 12:10 13:10
Tuesday 11:10 12:10
Prof Adrienne Nicotra
6125 9763

Research Interests

Genomic and high-throughput sequencing experiments; scientific computing; experimental designs in biology.

Prof Adrienne Nicotra

By Appointment
Dr Marcin Adamski
6125 2761

Research Interests

Dr Marcin Adamski

Monday 12:10 13:10
Monday 12:10 13:10
Tuesday 11:10 12:10
Dr Teresa Neeman
612 54033

Research Interests

Genomic and high-throughput sequencing experiments; scientific computing; experimental designs in biology.

Dr Teresa Neeman

By Appointment

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions