Experimental Design and Data Analysis (MAST10011)

Undergraduate level 1Points: 12.5On Campus (Parkville)

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Overview

Availability(Quotas apply)
Semester 1
Semester 2
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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

Eligibility and requirements

Prerequisites

None

Corequisites

None

Non-allowed subjects

Students may only gain credit for one of:

  • Code Name Teaching period Credit Points
    MAST10010 Data Analysis 1
    Semester 2 (On Campus - Parkville)
    		 12.5
     Data Analysis 1
  • Code Name Teaching period Credit Points
    MAST10011 Experimental Design and Data Analysis
    Semester 2 (On Campus - Parkville)
    Semester 1 (On Campus - Parkville)
    		 12.5
    Experimental Design and Data Analysis
  • Code Name Teaching period Credit Points
    ECON10005 Quantitative Methods 1
    Semester 2 (On Campus - Parkville)
    Semester 1 (On Campus - Parkville)
    		 12.5
    Quantitative Methods 1

Students who have completed 

Code Name Teaching period Credit Points
MAST20005 Statistics
Semester 2 (On Campus - Parkville)
 12.5
Statistics may not enrol in this subject for credit.

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 31 October 2023

Assessment

Additional details

  • 1 Hand-written assignments (part 1) with use of software due weeks 3 and 4 (5%)
  • 1 Hand-written assignments (part 2) with use of software due weeks 7 and 8 (5%)
  • One 40 minute computer-based in-class test due week 11 (10%)
  • Best 10 (of 11) online quizzes held weekly from week 2 (10%)
  • 3-hour written examination in the examination period (70%)

Last updated: 31 October 2023

Quotas apply to this subject

Dates & times

  • Semester 1
    Principal coordinatorYao-Ban Chan
    Mode of deliveryOn Campus (Parkville)
    Contact hours3 x one hour lectures per week, 1 x one hour practice class per week, 1 x one hour computer laboratory class per week
    Total time commitment170 hours
    Teaching period26 February 2018 to 27 May 2018
    Last self-enrol date 9 March 2018
    Census date31 March 2018
    Last date to withdraw without fail 4 May 2018
    Assessment period ends22 June 2018

    Semester 1 contact information

    Email: yaoban@unimelb.edu.au

  • Semester 2
    Principal coordinatorRheanna Mainzer
    Mode of deliveryOn Campus (Parkville)
    Contact hours3 x one hour lectures per week, 1 x one hour practice class per week, 1 x one hour computer laboratory class per week
    Total time commitment170 hours
    Teaching period23 July 2018 to 21 October 2018
    Last self-enrol date 3 August 2018
    Census date31 August 2018
    Last date to withdraw without fail21 September 2018
    Assessment period ends16 November 2018

    Semester 2 contact information

    Email: rheanna.mainzer@unimelb.edu.au

Time commitment details

Estimated total time commitment of 170 hours

Additional delivery details

An enrolment quota of 350 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.

Last updated: 31 October 2023

Further information

  • Texts

    Prescribed texts

    None.

    Recommended texts and other resources

    B. Rosner, Fundamentals of Biostatistics, 8th Edition, Cengage Learning, 2015.

  • Subject notes

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

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseBachelor of Biomedicine
  • Available through the Community Access Program

    About the Community Access Program (CAP)

    This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.

    Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants

    Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.

  • Available to Study Abroad and/or Study Exchange Students

    This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.

Last updated: 31 October 2023