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Experimental Design and Data Analysis (MAST10011)

Undergraduate level 1Points: 12.5On Campus (Parkville)

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Overview

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Semester 1
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This subject provides an understanding of the fundamental concepts of probability and statistics required for experimental design and data analysis in the health sciences. Initially the subject introduces common study designs, random sampling and randomised trials as well as numerical and visual methods of summarising data. It then focuses on understanding population characteristics such as means, variances, proportions, risk ratios, odds ratios, rates, prevalence, and measures used to assess the diagnostic value of a clinical test. Finally, after determining the sampling distributions of some common statistics, confidence intervals will be used to estimate these population characteristics and statistical tests of hypotheses will be developed. The presentation and interpretation of the results from statistical analyses of typical health research studies will be emphasised.

The statistical methods will be implemented using a standard statistical computing package and illustrated on applications from the health sciences.

Intended learning outcomes

On completion of the subject, students should be able to:



  • analyse standard data sets, interpreting the results of such analysis and presenting the conclusions in a clear and comprehensible manner;
  • understand a range of standard statistical methods which can be applied to biomedical sciences.
  • use a statistical computing package to analyse biomedical data;
  • choose a form of epidemiological experimental design suitable for a range of standard biomedical experiments.

Generic skills

In addition to learning specific skills that will assist students in their future careers in the health sciences, they will have the opportunity to develop, generic skills that will assist them in any future career path. These include:

  • problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
  • analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
  • collaborative skills: the ability to work in a team;
  • time management skills: the ability to meet regular deadlines while balancing competing commitments;
  • computer skills: the ability to use statistical computing packages.

Last updated: 31 October 2023