Error is protean, ubiquitous and crucial in scientific process. In this paper it is argued that understanding scientific process requires what is currently absent: an adaptable, context-sensitive functional role for error in science that naturally harnesses error identification and avoidance to positive, success-driven, science. This paper develops a new account of scientific process of this sort, error and success driving Self-Directed Anticipative Learning (SDAL) cycling, using a recent re-analysis of ape-language research as test example. The example shows the limitations of (...) other accounts of error, in particular Mayo’s (Error and the growth of experimental knowledge, 1996) error-statistical approach, and SDAL cycling shows how they can be fruitfully contextualised. (shrink)

This essay is concerned with the epistemic roles of error in scientific practice. Usually, error is regarded as something negative, as an impediment or obstacle for the advancement of science. However, we also frequently say that we are learning from error. This common expression suggests that the role of error is not—at least not always—negative but that errors can make a fruitful contribution to the scientific enterprise. My paper explores the latter possibility. Can errors play an epistemically productive role in (...) scientific research? The paper begins with a review of several twentieth-century approaches to error and the various agendas behind them. It is shown that only very few scholars have considered whether errors can be productive. The main part of the paper examines a concrete debate in early nineteenth-century microscopy and analyses how the microscopists coped with the problem of error. Drawing on this material, the article offers some terminological clarifications of the common notion ‘learning from error’. The conclusion argues that error can indeed play epistemically productive roles in scientific practice.Keywords: Error; Nineteenth-century microscopic anatomy. (shrink)

The relation between models and the world is mediated by experimental procedures generating data that are used as evidence to evaluate the model. Data can serve as empirical evidence, for or against, only if they result from reliable experimental procedures. The aim of this article is to discuss the role of relevance judgments in the evaluation of reliability and to clarify the conditions under which reliability can be a strictly empirical matter. It is argued that reliability is a strictly empirical (...) issue only in the restricted case in which the claim under test/investigation is about a data-generating procedure. (shrink)