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. (shrink)

This paper explores the relationship between concepts of probability and hermeneutics. It seeks to examine the relationship between subjective (Bayesian) views of probability and hermeneutic descriptions of understanding. It is argued that Gadamer'saccount of the prejudicial nature of understanding, derived from Heidegger'sanalysis of foreunderstanding, offers a provocative model of clinical reasoning. The implications of this model for evidence-based medicine are discussed in conclusion.

In the shadowy world between philosophy of science and ethics lie the paired concepts of underdetermination and incommensurability. Typically, scientific evidence underdetermines the hypotheses tested in research studies, providing neither proof nor disproof. As a result, scientists must judge the weight of the evidence, and in doing so, bring scientific and extrascientific values to bear in their approaches to assessing and interpreting the evidence. When different scientists employ very different values, their views are said to be incommensurable. Less prominent differences (...) represent partial incommensurabilities. The definitions and analyses provided by McMullin and by Veatch and Stempsey lay the foundation for the description of partial incommensurabilities in the current practice of assessing and interpreting epidemiologic evidence. This practice is called "causal inference" and is undertaken for the purpose of making causal conclusions and public health recommendations from population-based studies of exposures and diseases. Following the work of Bayley and Longino, several suggestions are examined for dealing with the partial incommensurabilities found in the general practice of causal inference in contemporary epidemiology. Two specific examples illustrate these ideas: studies on the relationship between induced abortion and breast cancer and those on the relationship between moderate alcohol consumption and breast cancer. (shrink)