Principles of Statistical Inference (PSI)

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.

Ms Liz Barnes University of Sydney, NHMRC Clinical Trials Centre Semester 1
Dr Erin Cvejic University of Sydney, Sydney School of Public Health Semester 2
General outline


Mathematical Foundations for Biostatistics

Time commitment

8-12 hours total study time per week

Semester availability

Semester 1 & 2


Two major assignments worth 40% each and module exercises worth a total of 20%

Prescribed Texts

Marschner IC. Inference Principles for Biostatisticians. Chapman & Hall / CRC Pr, 2014

Special Computer Requirements

R or Stata statistical software


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.

Special Computer Requirements

Course notes, online mini-lecture videos, online tutorials, discussion board