This study discusses how perceptions of ethics are formed by certified public accountants (CPAs). Theologians are used as a point of comparison. When considering CPA ethical dilemmas, both subject groups in this research project viewed confidentiality and independence as more important than recipient of responsibility and seriousness of breach. Neither group, however, was insensitive to any of the factors presented for its consideration. CPA reactions to ethical dilemmas were governed primarily by provisions of the CPA ethics code; conformity to that (...) code may well be evidence of higher stage moral reasoning. (shrink)
In the past, hypothesis testing in medicine has employed the paradigm of the repeatable experiment. In statistical hypothesis testing, an unbiased sample is drawn from a larger source population, and a calculated statistic is compared to a preassigned critical region, on the assumption that the comparison could be repeated an indefinite number of times. However, repeated experiments often cannot be performed on human beings, due to ethical or economic constraints. We describe a new paradigm for hypothesis testing which uses only (...) rearrangements of data present within the observed data set. The token swap test, based on this new paradigm, is applied to three data sets from cardiovascular pathology, and computational experiments suggest that the token swap test satisfies the Neyman Pearson condition. (shrink)
Philosophers and statisticians have been debating on causality for a long time. However, these discussions have been led quite independently from each other. An objective of this paper is to pursue a fruitful dialogue between philosophy and statistics. As is well known, at the beginning of the 20th century, some philosophers and statisticians dismissed the concept of causality altogether. It will suffice to mention Bertrand Russell (1913) and Karl Pearson (1911). Almost a hundred years later, causality still represents a (...) central topic both in philosophy and statistics. In the social sciences, including research on public health, most studies are concerned with the possible causes, determinants, factors, etc. of a set of observations. In particular, for planning or policy reasons, it is important to know what causes which effects. In order to attain causal knowledge, many social scientists appeal to statistical modelling to confirm or disconfirm their hypotheses about possible causal relations among the variables they consider, taking care of controlling for relevant covariates and especially for possible confounding factors. To what extent can a statistical model say something about causal relations among variables? In this paper, we will attempt an answer by examining a special class of statistical models, i.e. structural models. The discussion, however, will not be confined to a mere examination of statistical methods, since a considerable effort will be made to consider causality from an epistemological perspective. To put it otherwise, this paper does not address the nature of causation itself, nor the analysis of various causal structures, nor the elaboration of complex causal structures; rather, we will be concerned with the question of how we come to uncover causal relations by means of statistical modelling. The practice of statistical modelling raises substantial issues of ontological nature.. (shrink)
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