1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Verification and validation of numerical models of natural systems is impossible. This is because natural systems are never closed and because model results are always nonunique. Models can be confirmed by the demonstration of agreement between observation and prediction, but confirmation is inherently partial. Complete confirmation is logically precluded by the fallacy of affirming the consequent and by incomplete access to natural phenomena. Models can only be evaluated in relative terms, and their predictive value is always open to question. The primary value of models is heuristic.

https://science.sciencemag.org/content/sci/263/5147/641.full.pdf

Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences

Criticism and the Growth of Knowledge. Edited by I. Lakatos and A. Musgrave. Pp. viii, 282. £3·50, paperback £1. 1970. (Cambridge University Press.)

https://core.ac.uk/download/pdf/374223.pdf

A manifesto for the equifinality thesis

In the field of psychology, the practice ofp value null-hypothesis testing is as widespread as ever. Despite this popularity, or perhaps because of it, most psychologists are not aware of the statistical peculiarities of thep value procedure. In particular,p values are based on data that were never observed, and these hypothetical data are themselves influenced by subjective intentions. Moreover,p values do not quantify statistical evidence. This article reviews thesep value problems and illustrates each problem with concrete examples. The three problems are familiar to statisticians but may be new to psychologists. A practical solution to thesep value problems is to adopt a model selection perspective and use the Bayesian information criterion (BIC) for statistical inference (Raftery, 1995). The BIC provides an approximation to a Bayesian hypothesis test, does not require the specification of priors, and can be easily calculated from SPSS output.

/pdf/a-practical-solution-to-the-pervasive-problems-of-p-values-1qozascdn9.pdf

A practical solution to the pervasive problems of p values.

This is an updated, revised and enlarged edition of Howson and Urbach's account of scientific method from the Bayesian standpoint. The book offers both an introduction to probability theory and a philosophical commentary on scientific inference. This new edition includes chapter exercises and extended material on topics such as regression analysis, distributions densities, randomisation and conditionalisation.

Scientific Reasoning: The Bayesian Approach.

Scientific Reasoning: The Bayesian Approach

9. Scientific Reasoning: the Bayesian Approach. By C. Howson and P. Urbach. ISBN 0 8126 9084 2 (hardbound), 0 8126 9085 0 (paperbound). Open Court, la Salle, 1989. 312 pp. $34.95 (hardbound), $16.95 (paperbound).

The hereditarian programme makes no predictions concerning the relative average intelligence of different racial or social groups. It is perfectly consistent with any observed differences (or lack of them) in IQ between different groups. The environmentalist programme, on the other hand, predicts that all groups will be equal in innate mental ability; any IQ differences which cannot be explained by environmental factors are consequently anomalies. In this section I shall discuss some environmental theories which have

Progress and Degeneration in the 'IQ Debate' (II)

Bayesian scientific reasoning has a sound foundation in logic and provides a unified approach to the evaluation of deterministic and statistical theories, unlike its main rivals.

Peter Urbach

Papers

Scientific Reasoning: The Bayesian Approach.

Scientific Reasoning: The Bayesian Approach

Scientific Reasoning: The Bayesian Approach.

Progress and Degeneration in the 'IQ Debate' (II)

Bayesian reasoning in science