Accessibility Skip to Global Navigation Skip to Local Navigation Skip to Content Skip to Search Skip to Site Map Menu

ZOOL316 Biological Data Analysis and Computing

Due to COVID-19 restrictions, a selection of on-campus papers will be made available via distance and online learning for eligible students.
Find out which papers are available and how to apply on our COVID-19 website

Uses real biological examples and computers, and deals with types of data and their acquisition; graphical and exploratory analysis; estimation and hypothesis testing; experimental design; computer-intensive methods and simulation.

This paper covers experimental design and data analysis techniques widely used in the biological sciences, taught using the free software R.

Paper title Biological Data Analysis and Computing
Paper code ZOOL316
Subject Zoology
EFTS 0.15
Points 18 points
Teaching period Semester 1 (On campus)
Domestic Tuition Fees (NZD) $1,110.75
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

(STAT 110 or STAT 115) and 54 200-level points from Science Schedule C
WILM 404
Schedule C
Teaching staff

Professor Christoph Matthaei
Dr Stephanie Godfrey
Associate Professor Michael Paulin

Paper Structure
24 lectures (30-45 minutes), with 24 corresponding tutorials (60-75 minutes) involving hands-on programming and data analysis using R, which are taught in computer labs (assisted by student demonstrators).
Teaching Arrangements
The first course module (3 lecture/tutorial sessions) provides an in-depth training in experimental design.

The second course module (9 sessions in total) covers fundamental statistical issues (4 sessions), simple analyses (2 sessions) and complex analyses (3 sessions).

Modules 3 and 4 (12 sessions in total) cover mainly intermediate and advanced techniques complementing Module 2.
Quinn, G.P. and Keough, M.J. (2002) Experimental Design and Data Analysis for Biologists. Cambridge University Press, Cambridge, UK.

Whitlock, M.C. and Schluter, D. (2009) The Analysis of Biological Data. Roberts & Co. Publishers, Colorado, USA.

Kruschke, J.K. (2011) Doing Bayesian Data Analysis: A tutorial with R and BUGS. Elsevier.
Graduate Attributes Emphasised
Interdisciplinary perspective, Lifelong learning, Scholarship, Critical thinking, Information literacy, Research.
View more information about Otago's graduate attributes.
Learning Outcomes
Students will gain an understanding of key issues related to experimental design and data analysis. They will also learn to use the free software R to conduct a range of analyses (from basic to complex).

Specific aims of the paper include:
  • Helping you design field or laboratory experiments
  • Helping you gather, present and interpret biological data
  • Enabling you to make recommendations and decisions about biological systems
  • Giving you a foundation for understanding, critically evaluating and using statistical data
  • Building up your knowledge slowly and thoroughly
  • Hopefully, helping reduce a possible fear or dislike of stats!

^ Top of page


Semester 1

Teaching method
This paper is taught On Campus
Learning management system

Computer Lab

Stream Days Times Weeks
A1 Monday 11:00-11:50 9-15, 18-22
Thursday 12:00-12:50 9-15, 17-22


Stream Days Times Weeks
A1 Monday 10:00-10:50 9-15, 18-22
Thursday 11:00-11:50 9-15, 17-22