Experimental Design and Data Analysis

This subject has the following teaching availabilities in 2015:

An enrolment quota of 270 students applies per semester. Students will be enrolled in Experimental Design and Data Analysis in the opposite semester to which they are enrolled in Mathematics for Biomedicine. Please refer to the Handbook entry for MAST10016 Mathematics for Biomedicine for further information.

Estimated total time commitment of 170 hours

Students may only gain credit for one of

• MAST10010 Data Analysis 1
• MAST10011 Experimental Design and Data Analysis
• ECON10005 Quantitative Methods 1

Students who have completed any of the following may not enrol in this subject for credit

• MAST20005 Statistics
• MAST20017 Applied Statistics for Optometrists (prior to 2012)

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability

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.

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.

One written assignment of up to 10 pages due in the second half of semester (5%), eight to twelve homework quizzes (a combination of written and oneline) due at regular intervals during semester (10%), one 45-minute written computer laboratory test held during semester (5%), and a 3-hour written examination in the examination period (80%).

None.

M. M. Triola and M. F. Triola, Biostatistics for the Biological and Health Sciences, Boston, Pearson, 2006.

This subject is not available as a breadth subject.

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.

This subject is only available to students enrolled in the Bachelor of Biomedicine degree or the Bachelor of Biomedical Science (pre-2008 degree)