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Overview

Course Prescription

Provides an overview of statistics and statistical methods for health scientists. Covers a range of methods and tests, including regression.

Course Overview

The course covers understanding, practical application, and interpertation of essential statistics in health sciences. Practical skills in the use of the statistical analysis package R will be developed, including basic data management and cleaning, descriptive analysis and the use of common statistical tests and regression models. Students will learn how to interpret medical and epidemiological data and understand the statistics that are commonly reported in medical research reports.

Key Topics

Summarising & presenting data using R

Estimation, Confidence intervals, statistical testing and p-values

Measures of contrast (Relative Risk, Odds Ratio, Risk Difference), Chi-square and Fishers' exact test

Common statistical tests for continuous variables (t-test, ANOVA, post-hoc tests, parametric test assumptions, non-parametric tests, correlation coefficients)

Linear regression

Logistic Regression

Sample size & statistical power

Course Contacts

Course director: Dr Alana Cavadino, Section of Epidemiology and Biostatistics, School of Population Health.Email: a.cavadino@auckland.ac.nz 
Course co-lecturer: Dr Arier Lee, Section of Epidemiology and Biostatistics, School of Population Health.Email: arier.lee@auckland.ac.nz
Course administrator: Upendra Wickramarachchi, School of Population HealthTel: (09) 923 3058, Email: u.wicks@auckland.ac.nz

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect  24 hours of lectures, 8 hours tutorial, 10 hours of reading and thinking about the content and 10 hours of work on assignments and/or test preparation.

Locations and Semesters Offered

LocationSemester
Grafton

Teaching and Learning

Campus Experience

Attendance is expected at scheduled activities including tutorials.

The course will not include live online events. Lectures will be available as recordings. Other learning activities including tutorials will not be available as recordings. 

The activities for the course are scheduled as block delivery, with teaching between 8am and 1pm on 8 Wednesdays across semester 1.

Each teaching day includes lectures from 8-11am, a break from 11am-12pm, and a practical session in a computer lab from 12-1pm. 

Teaching dates for 2026: 04 March, 11 March, 25 March, 01 April, 22 April, 06 May, 20 May, 03 June.

Learning Resources

Taught courses use a learning and collaboration tool called Canvas to provide students with learning materials including reading lists and lecture recordings (where available). Please remember that the recording of any class on a personal device requires the permission of the instructor.

Additional Information on Learning Resources

Copies of all PowerPoint slides used in the lectures will be provided, as well as supplementary notes and other learning material. Lectures will also be available as recordings. These will be sufficient for the course and there is no set textbook.
However, students may find benefit in other sources. There are many reference texts and resources for further reading covering concepts taught in this course as well as more in-depth material on related topics that are beyond the scope of this course. Some examples include:

  • Primer of biostatistics. Textbook by Stanton A Glantz.
  • Medical statistics from scratch: an introduction for health professionals . Textbook by David Bowers. Full text available online via the UoA library catalogue.
  • The Epidemiologist R Handbook, 2021. Online resource by Batra, Neale, et al: https://epirhandbook.com/
  • Quick-R. Online resource: https://www.statmethods.net/. Linked to: R in Action - Data analysis and graphics with R. Textbook by Robert I. Kabacoff that significantly expands upon the material in the Quick-R website. 
  • R for Data Science. Textbook by Hadley Wickham and Garrett Grolemund. Available online: https://r4ds.had.co.nz/index.html
  • R graphics. Textbook by Paul Murrell. Full text available online via the UoA library catalogue.
  • The R Book. Textbook by Michael J. Crawley. Full text available online via the UoA library catalogue.

Copyright

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license. You may copy the course content for the purposes of private study or research, but you may not upload onto any third-party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

Other Information

This course will use the R statistical package.  There will be introductory R materials available before the start of the semester, which will benefit students with no experience using this (or similar) software.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Similarly, research students must meet the University’s expectations of good research practice. This requires:

  • Honesty - in all aspects of research work
  • Accountability - in the conduct of research
  • Professional courtesy and fairness – in working with others
  • Good stewardship – on behalf of others
  • Transparency – of research process and presentation of results
  • Clarity - communication to be understandable, explainable and accessible

For more information on the University’s expectations of academic integrity, please see the Academic Conduct section of the University policy hub.

Disclaimer

Elements of this outline may be subject to change. The latest information about taught courses is made available to enrolled students in Canvas.

Students may be asked to submit assessments digitally. The University reserves the right to conduct scheduled tests and examinations online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.


Assessment and Learning Outcomes

Course Learning Outcomes

CLO #OutcomeProgramme Capability Link
1
2
3
4
5

Assessments

Assessment TypeAssessment PercentageAssessment Classification

Additional Information on Assessment

This is a Double Pass course. 50% pass in the final exam as well as 50% overall is required to pass this course.


Note: Dates for the semester one examination period can be found here https://www.auckland.ac.nz/en/students/academic-information/important-dates.html.

Examinations are scheduled Monday to Saturday. The examination timetables will not be finalised and available to students until 6-8 weeks into the semester.

Assessment to CLO Mapping

Assessment Type12345

Student Feedback, Support and Charter

Student Feedback

Feedback on taught courses is gathered from students at the end of each semester through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions. Your feedback helps teachers to improve the course and its delivery for future students. In addition, class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Additional Information on Student Feedback

Course materials are revised annually in response to student feedback.

Class representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Tuākana

Tuākana is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at Tuakana Learning Communities.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor. Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website.

Wellbeing

We all go through tough times during the semester, or see our friends struggling. There is lots of help out there - please see the Support Services page for information on support services in the University and the wider community.

Special Circumstances

If your ability to complete assessed work is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due. If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page. This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter.

Student Academic Complaints and Disputes

Students with concerns about teaching including how a course is delivered, the resources provided, or supervision arrangements, have the right to express their concerns and seek resolution. The university encourages informal resolution where possible, as this is quicker and less stressful. For information on the informal and formal complaints processes, please refer to the Student Academic Complaints Statute in the Student Policies and Guidelines section of the Policy Hub.