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Biometry (BIOL90002)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Year of offer2019
Subject levelGraduate coursework
Subject codeBIOL90002
FeesSubject EFTSL, Level, Discipline & Census Date

Biological knowledge is increased by an iterative process of developing ideas, collecting data to assess those ideas, analysing and interpreting those data, and communicating the conclusions. Those conclusions are used to develop new research ideas, improve human health, and to make decisions about environmental management. For this process to be successful, we must collect the right data, enough data, and we must analyse and interpret those data correctly. Biologists must also be able to interpret colleagues’ analyses and interpretation critically.

This subject provides recommendations on appropriate was of collecting data, introduces the most common statistical tools applied to biological (including biomedical and environmental) data, and discusses ways of interpreting and presenting the results of analyses. Topics covered include strategies for efficient and effective estimation, the design of routine monitoring and assessment programs, and experimental design. It will also cover the most common statistical methods used for biological data, including general linear models, logistic and log-linear models, and multivariate techniques, and emphasis will be placed on interpretation and reporting of data analyses.

Intended learning outcomes

The objectives of this subject are to provide students with:

  • familiarity with the kinds of data generated by biological and environmental research programs;
  • the skills to design efficient sampling programs and experiments in biological science ;
  • an awareness of biological issues that may cause statistical complications;
  • an understanding of the statistical models that are applied to different kinds of biological data;
  • be able to present and interpret results of analyses.

Generic skills

At the completion of this subject, students should gain skills in:

  • handling, managing and interpreting quantitative data;
  • communicating quantitative results to a general audience;
  • developing the ability to exercise critical judgement;
  • rigorous and independent thinking;
  • time management and self-management.

Last updated: 8 August 2019