EPM5003 · Principles of statistical inference

Overview

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.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

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

Workload

No workload detail published.

Student experience, not official rules. Nothing here changes what the Handbook says.