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APTS module: Statistical Inference

Module leader: J C Rougier

Please see the full Module Specifications PDF file document for background information relating to all of the APTS modules, including how to interpret the information below.

Aims: To explore the basic principles of statistical inference: its origins in decision support, the nature of statistical parameters, the different viewpoints of Bayesian and Frequentist approaches, and the meaning and value of ubiquitous constructs such as p-values, confidence sets, and hypothesis tests.

Learning outcomes: An appreciation for the complexity of statistical inference, a recognition of its inherent subjectivity and the role of expert judgement, knowledge of the key choices that must be made, and scepticism about apparently simple answers to difficult questions.

Preliminaries: Students should have done at least one course in probability and one in statistics. Preliminary reading will cover the necessary material on probability. For statistics, students should be familiar with: the idea of a statistical model, statistical parameters, the likelihood function, estimators, the maximum likelihood estimator, confidence intervals and hypothesis tests, p-values, Bayesian inference, prior and posterior distributions.

Further information on all of these topics can be found in standard undergraduate statistics textbooks, for example

  • J.A. Rice, 1999, Mathematical Statistics and Data Analysis, 2nd edn, Duxbury Press (more recent edition available); or
  • Morris H, DeGroot, and Mark J Schervish, 2002, Probability & Statistics, Addison Wesley, 3rd edn. Prof. Schervish maintains a list of errata at

More advanced treatments can be found in

  • G.A. Young and R.L. Smith, 2005, Essential of Statistical Inference, Cambridge University Press.
  • A.C. Davison, 2003, Statistical Models, Cambridge University Press. This book also contains a wealth of applications. Prof. Davison maintains a list of errata at


  1. What is statistics? Statistical models, prediction and inference, Frequentist and Bayesian approaches.
  2. Principles of inference: the Likelihood Principle, Birnbaum's Theorem, the Stopping Rule Principle, implications for different approaches.
  3. Decision theory: Bayes Rules, admissibility, and the Complete Class Theorems. Implications for point and set estimation, and for hypothesis testing.
  4. Confidence sets, hypothesis testing, and P-values. Good and not-so-good choices. Level error, and adjusting for it. Interpretation of small and large P-values.

Assessment: Exam-style questions on the implementation of different approaches in particular types of inference, possibly involving additional reading.