This unit entry is for students who completed this unit in 2016 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

EPM5003 - Principles of statistical inference

6 points, SCA Band 2, 0.125 EFTSL

Postgraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

Faculty

Medicine, Nursing and Health Sciences

Organisational Unit

Department of Epidemiology and Preventive Medicine

Coordinator(s)

Associate Professor Patrick Kelly

Offered

Alfred Hospital

First semester 2016 (Off-campus)
Second semester 2016 (Off-campus)

Synopsis

The unit will introduce the core concepts of statistical inference, beginning with estimators, confidence intervals, type I and II errors and p-values. The emphasis will be on the practical interpretation of these concepts in biostatistical contexts, including an emphasis on the difference between statistical and practical significance. Classical estimation theory, bias and efficiency. Likelihood function, likelihood based methodology, maximum likelihood estimation and inference based on likelihood ration, Wald and score test procedures. Bayesian approach to statistical inference vs classical frequentist approach. Nonparametric procedures, exact inference and resampling based methodology.

Outcomes

Upon successful completion of this unit, students should be able to:

Have a deeper understanding of fundamental concepts in statistical inference and their practical interpretation and importance in biostatistical contexts.
Understand the theoretical basis for frequentists and Bayesian approaches to statistical inference.
Be able to develop and apply parametric methods of inference, with particular reference to problems of relevance in biostatistical contexts.
Have the theoretical basis to understand the justification for more complex statistical procedures introduced in subsequent units.
Have an understanding of basic alternatives to standard likelihood-based methods, and be able to identify situations in which these methods are useful.