Students

STAT3191 – Statistical Inference for Data Science

2026 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convenor
Nino Kordzakhia
nino.kordzakhia@mq.edu.au
Contact via Email
Room 639, Level 6, 12 Wally's Walk
Consult iLearn for details.
Credit points Credit points
10
Prerequisites Prerequisites
STAT2170 and COMP2200
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

Statistical inference allows us to draw meaningful conclusions about a population by analysing a representative sample. This unit covers foundational probability concepts, providing the framework for using sample data to make inferences about the broader population. It then explores the theory and application of classical statistical inference techniques to quantify uncertainty and make informed decisions. The unit also introduces the Bayesian approach, which combines prior knowledge with sample data for a more holistic, subjective analysis, especially useful in areas with limited data or significant prior knowledge. The focus is on building a strong conceptual understanding, with practical examples to reinforce theory and highlight real-world relevance.

Learning in this unit enhances student understanding of global challenges identified by the United Nations Sustainable Development Goals (UNSDGs) Industry, Innovation and Infrastructure

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Explain and use Probability Theory relevant to Statistical Inference.
  • ULO2: Demonstrate knowledge of the fundamental principles of inference.
  • ULO3: Estimate key population parameters of interest, test hypotheses about them and construct confidence regions.
  • ULO4: Evaluate and use likelihood-based inference methods.
  • ULO5: Evaluate and use elementary Bayesian inference methods.
  • ULO6: Analyse data using computing packages which implement the most common Inference procedures.

General Assessment Information

Requirements to Pass this Unit

To pass this unit you must:

  • Achieve a total mark equal to or greater than 50% across all assessments.

 

Classes

Weekly class participation is expected throughout the session. Students are expected to attend all classes and actively participate in learning activities.

Participation

Regular engagement is crucial for your success in this unit, as these activities provide opportunities to deepen your understanding of the material, collaborate with peers, and receive valuable feedback from instructors to help you complete the unit assessments.

Hurdle Assessments

There is no hurdle assessment.

Late Assessment Submission Penalty

  • 5% penalty per day: If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days.

Example 1 (out of 100): If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100.

Example 2 (out of 30): If you score 27/30 but submit 1 day late, you will lose 1.5 marks and receive 25.5/30.

  • After 7 days: Submissions more than 7 days late will receive a mark of 0.
  • Extensions:
    • Short extension: Some assessments are eligible for automatic Short Extension. You can only apply for an automatic short extension before the due date.
    • Special Consideration: If you need more time due to serious issues and for any assessments that are not eligible for Short Extension, you must apply for Special Consideration. You can find the requirements for applying for Special Consideration HERE.

ASSESSMENTS WHERE LATE SUBMISSIONS WILL BE ACCEPTED:

Statistical Inference Problem Set – YES, Late Assessment Submission Penalty applies.

Project Report – YES, Late Assessment Submission Penalty applies. This assessment is NOT eligible for a Short Extension.

Final Exam – NO, unless Special Consideration is granted. 

Special Consideration

The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through https://connect.mq.edu.au.

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI Approach
Statistical Inference Problem Set 20% No 29/03/2026 Individual Yes Open
Project Report 30% No 24/05/2026 Group No Open
Final Exam 50% No University Examination Period Individual No Observed

Statistical Inference Problem Set

Assessment Type 1: Problem-based task
Indicative Time on Task 2: 15 hours
Due: 29/03/2026
Weighting: 20%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

Students will be given a set of problems to complete on their own as a take-home assessment. In this assessment, students will reinforce and apply the concepts covered in lectures, along with the skills developed in SGTA sessions.
On successful completion you will be able to:
  • Explain and use Probability Theory relevant to Statistical Inference.
  • Demonstrate knowledge of the fundamental principles of inference.
  • Estimate key population parameters of interest, test hypotheses about them and construct confidence regions.

Project Report

Assessment Type 1: Written Submission
Indicative Time on Task 2: 30 hours
Due: 24/05/2026
Weighting: 30%
Groupwork/Individual: Group
Short extension 3: No
AI Approach: Open

A written report must be submitted, in which students will demonstrate their practical skills by applying statistical techniques to a simulation-based inference problem.
On successful completion you will be able to:
  • Explain and use Probability Theory relevant to Statistical Inference.
  • Demonstrate knowledge of the fundamental principles of inference.
  • Estimate key population parameters of interest, test hypotheses about them and construct confidence regions.
  • Evaluate and use likelihood-based inference methods.
  • Evaluate and use elementary Bayesian inference methods.
  • Analyse data using computing packages which implement the most common Inference procedures.

Final Exam

Assessment Type 1: Examination
Indicative Time on Task 2: 25 hours
Due: University Examination Period
Weighting: 50%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed

An invigilated examination held during the University’s formal examination period.
On successful completion you will be able to:
  • Explain and use Probability Theory relevant to Statistical Inference.
  • Demonstrate knowledge of the fundamental principles of inference.
  • Estimate key population parameters of interest, test hypotheses about them and construct confidence regions.
  • Evaluate and use likelihood-based inference methods.
  • Evaluate and use elementary Bayesian inference methods.
  • Analyse data using computing packages which implement the most common Inference procedures.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • Academic Success for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

Delivery and Resources

Classes

Lectures (beginning in Week 1): A one-hour lecture each week.

SGTA classes (beginning in Week 2): A two-hour SGTA class each week. Students must register for the SGTA class.

Students can use the Class Finder tool in eStudent to see when and where classes are being held via Publish: https://publish.mq.edu.au/.

Enrolment can be managed using eStudent at: https://students.mq.edu.au/support/technology/systems/estudent.

Computing and Software

R, RStudio, and Quarto are freely available for download and will be used in the SGTA sessions and assessment tasks for this unit.

Recommended References:

  • Wasserman, Larry. All of Statistics: A Concise Course in Statistical Inference. Springer Texts in Statistics, Springer New York, 2004.
  • Casella, George, and Roger L. Berger. Statistical Inference. 2nd ed., Duxbury, 2002.

Methods of Communication

We will communicate with you via your university email and through announcements on iLearn. Queries to convenors can either be placed on the iLearn discussion board or sent to the unit convenor via the contact email on iLearn.

Unit Schedule

Week 1: Introduction to statistical inference; fundamental concepts of probability; basic set theory.

Week 2: Random variables; discrete and continuous probability distributions; joint, marginal and conditional probabilties; independence.

Week 3: Common probability distributions; expectations and other key moments.

Week 4: Sequences of random variables; modes of convergence.

Week 5: Statistical models and estimation; sampling; properties of estimators; introductory estimation methods.

Week 6: Introduction to likelihood; key likelihood concepts.

Week 7: Maximum likelihood estimation (MLE); computation, properties and inference with MLE.

Week 8: Additional properties of estimators; miminum variance estimators; confidence intervals.

Week 9: Standard hypothesis testing.

Week 10: Likelihood-based hypothesis testing.

Week 11: The Bayesian paradigm; Bayes' theorem; Bayesian inference.

Week 12: Prior Specification; conjugate priors; maximum posteriori estimates; credible intervals.

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Academic Success

Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via the Service Connect Portal, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Changes from Previous Offering

We value student feedback to be able to continually improve the way we offer our units. 

Student feedback from the previous offering of this unit was very positive overall. As such, no change to the delivery of the unit is planned, however we will continue to strive to improve the level of support and the level of student engagement.

Unit information based on version 2026.01R of the Handbook