title: Further statistics in dentistry. Part 9: Bayesian statistics.
abstract: Statistics can be defined as the methods used to assimilate data, so that guidance can be given, and conclusions drawn, in situations which involve uncertainty. In particular, statistical inference is concerned with drawing conclusions about particular aspects of a population when that population cannot be studied in full. Uncertainty arises here because the totality of the information is not available. Instead, to make inferences about the population, it is necessary to rely on a sample of data which is selected from the population; this sample data may be augmented, in certain circumstances, by auxiliary information which is obtained independently of the sample data. Clearly, uncertainty lies at the heart of statistics and statistical inference. This uncertainty is measured by a probability which therefore forms the crux of statistics and must be properly understood in order to interpret a statistical analysis.
authors.affiliation: Eastman Dental Institute for Oral Health Care Sciences, University College London. a.petrie@eastman.ucl.ac.uk
authors.forename: A
authors.initials: A
authors.lastname: Petrie
authors.forename: J S
authors.initials: JS
authors.lastname: Bulman
authors.forename: J F
authors.initials: JF
authors.lastname: Osborn
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D003813:Dentistry / Q000706:statistics & numerical data*; D003945:Diagnosis, Oral / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D008403:Mass Screening / Q000706:statistics & numerical data; D011336:Probability