Principles of Statistical Inference


Introduces the core concepts of statistical inference, including estimators, confidence intervals, Type I & II errors and p-values. The emphasis is on the practical interpretation of these concepts in biostatistical contexts, including an emphasis on the difference between statistical and practical significance.

This course is offered in conjunction with the Biostatistics Collaboration of Australia (BCA)


Distance Education - Callaghan

  • Semester 1 - 2016
  • Semester 2 - 2016

Learning Outcomes

1. Have a deeper understanding of fundamental concepts in statistical inference and their practical interpretation and importance in biostatistical contexts

2. Understand the theoretical basis for frequentist and Bayesian approaches to statistical inference

3. Be able to apply likelihood-based methods of inference, with particular reference to problems of relevance in biostatistical contexts.


The course begins by introducing core concepts of statistical inference, including estimators, confidence intervals, Type I & II errors, and p-values. Concepts in classical estimation theory, including bias and efficiency are discussed. The course will then move on to a general study of the likelihood function, which will be used as a basis for the study of likelihood based methodology, including maximum likelihood estimation and inference based on likelihood ratio, Wald and score test procedures. The Bayesian approach to statistical inference will be studied and contrasted with the classical frequentist approach. Additional areas of study will include nonparametric procedures, exact inference and resampling based methodology.

Assessment Items

Written Assignment: Essays / Written Assignments

Written Assignment: Practical Written Exercises

Contact Hours

Distance Education - Callaghan

Self-Directed Learning

Self-Directed 6 hour(s) per Week for Full Term

Suggest 8-12 hours per week time commitment (guide only).