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Unit of study_

BSTA5002: Principles of Statistical Inference (PSI)

Semester 1, 2024 [Online] - Camperdown/Darlington, Sydney

The aim of this unit is to develop a strong mathematical and conceptual foundation in the methods of statistical inference, which underlie many of the methods utilised in subsequent units of study, and in biostatistical practice. The unit provides an overview of the concepts and properties of estimators of statistical model parameters, then proceeds to a general study of the likelihood function from first principles. This will serve as the basis for likelihood-based methodology, including maximum likelihood estimation, and the likelihood ratio, Wald, and score tests. Core statistical inference concepts including estimators and their ideal properties, hypothesis testing, p-values, confidence intervals, and power under a frequentist framework will be examined with an emphasis on both their mathematical derivation, and their interpretation and communication in a health and medical research setting. Other methods for estimation and hypothesis testing, including a brief introduction to the Bayesian approach to inference, exact and non-parametric methods, and simulation-based approaches will also be explored.

Unit details and rules

Unit code BSTA5002
Academic unit Public Health
Credit points 6
BSTA5100 or (BSTA5001 and BSTA5023)
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Liz Barnes,
Type Description Weight Due Length
Small continuous assessment Module exercises
One exercise for submission per Module (best 5 to count 4% each)
20% Multiple weeks 2-3 pages per Module
Outcomes assessed: LO1 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Assignment 1
Multiple questions covering material in Modules 1-3
40% Week 08
Due date: 21 Apr 2024 at 23:59
8-10 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment Assignment 2
Multiple questions covering material from all modules (Modules 1-6)
40% Week 13
Due date: 09 Jun 2024 at 23:59
8-10 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9

Assessment summary

  • 6 submissible module exercises, of which the best 5 module marks will each contribute towards the total mark.
  • 1 major written assignment covering Modules 1 to 3
  • 1 major written assignment covering Modules 1 to 6 (with a focus on Modules 4 to 6)

Detailed information for each assessment can be found on Canvas

Assessment criteria


Mark Range



Absent fail

Range from 0 to 49

To be awarded to students who fail to demonstrate the learning outcomes for the unit at an acceptable standard through failure to submit or attend compulsory assessment tasks or to attend classes to the required level. 



Range from 0 to less than 50

To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard.



Range from 50 to less than 65

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard.



Range from 65 to less than 75

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard.



Range from 75 to less than 85

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard.


High distinction

Range from 85 to 100 inclusive

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard.

* Your best five module marks will each contribute 4% towards the total of 20% for the module exercises


For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Standard BCA policy for late penalties for submitted work is a 5% deduction from the earned mark for each day the assessment is late, up to a maximum of 50%.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Multiple weeks Module 1 - Estimation concepts Independent study (20 hr) LO1
Module 2 - Hypothesis testing concepts Independent study (20 hr) LO2
Module 3 - Likelihood and estimation methods Independent study (20 hr) LO3 LO4 LO5
Module 4 - Hypothesis testing methods Independent study (20 hr) LO6
Module 5 - Bayesian inference Independent study (20 hr) LO7 LO8
Module 6 - Further inference topics Independent study (20 hr) LO9

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings


Marschner, I.C.(2014). Inference Principles for Biostatisticians. Chapman and Hall / CRC. ISBN: 9781482222234.

Note, a digital copy of this text may be available through the university library if you do not wish to purchase a copy. 


Sterne, J.A., Smith, G.D. (2001). Shifting the evidence-what's wrong with significance tests? BMJ, 322: 226-231.

Reese, R.A. (2004). Does significance matter? Significance, 1: 39-40.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Calculate and interpret important properties of point and interval estimators
  • LO2. Calculate and interpret p-values, power and confidence intervals correctly.
  • LO3. Write a likelihood function.
  • LO4. Derive and calculate the maximum likelihood estimate.
  • LO5. Derive and calculate the expected information.
  • LO6. Derive a Wald test, Score test and likelihood ratio test.
  • LO7. Use a Bayesian approach to derive a posterior distribution.
  • LO8. Calculate and interpret posterior probabilities and credible intervals.
  • LO9. Apply and explain an exact method, non-parametric and sampling-based method.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities

This section outlines changes made to this unit following staff and student reviews.

PSI is delivered in both Semester 1 and Semester 2 each year. From Semester 1 2022, there were major changes to some of the core units in the BCA program. PSI has added additional material in Module 1 – this material was previously covered in PDT and therefore may be revision for some students. Other PSI modules have also been rearranged. Based on feedback from previous deliveries, we have introduced recorded video lectures to complement the textbook readings, recorded worked video solutions to the non-assessed module exercises to further reinforce concepts, and provide the opportunity for live consultation (either in the form of tutorial or Q&A sessions, depending on module content) via videoconferencing to increase engagement and interactivity with the teaching team.

This course is delivered as part of the Biostatistics Collaboration of Australia (BCA).

Required Mathematical Background: Students should be familiar with the mathematical background covered as part of BSTA5001 or BSTA5100, including basic factorisation, rules for exponents and natural logarithms, differentiation and partial differentiation, and basic matrix manipulations (e.g., inverse of a matrix).

Software Requirements: Students will need to have access to either R or Stata to complete exercises and assignments. 


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