Philosophers of science diverge on the question what drives the growth of scientific knowledge. Most of the twentieth century was dominated by the notion that theories propel that growth whereas experiments play secondary roles of operating within the theoretical framework or testing theoretical predictions. New experimentalism, a school of thought pioneered by Ian Hacking in the early 1980s, challenged this view by arguing that theory-free exploratory experimentation may in many cases effectively probe nature and potentially spawn higher evidence-based theories. Because (...) theories are often powerless to envisage workings of complex biological systems, theory-independent experimentation is common in the life sciences. Some such experiments are triggered by compelling observation, others are prompted by innovative techniques or instruments, whereas different investigations query big data to identify regularities and underlying organizing principles. A distinct fourth type of experiments is motivated by a major question. Here I describe two question-guided experimental discoveries in biochemistry: the cyclic adenosine monophosphate mediator of hormone action and the ubiquitin-mediated system of protein degradation. Lacking underlying theories, antecedent data bases, or new techniques, the sole guides of the two discoveries were respective substantial questions. Both research projects were similarly instigated by theory-free exploratory experimentation and continued in alternating phases of results-based interim working hypotheses, their examination by experiment, provisional hypotheses again, and so on. These two cases designate theory-free, question-guided, stepwise biochemical investigations as a distinct subtype of the new experimentalism mode of scientific enquiry. (shrink)

Biased research occurs frequently in the sciences. In this paper, I will focus on one particular kind of biased research: research that is subject to sponsorship bias. I will address the following epistemological question: what precisely is epistemically wrong with biased research of this kind? I will defend the evidential account of epistemic wrongness: that is, research affected by sponsorship bias is epistemically wrong if and only if the researchers in question make false claims about the evidential support of some (...) hypothesis H by data E. I will argue that the evidential account captures the epistemic wrongness of three paradigmatic types of sponsorship bias. (shrink)

I examine, from Mayo's severe testing perspective, the case found in the Intergovernmental Panel on Climate Change fourth report for the claim that increases in anthropogenic greenhouse gas concentrations caused most of the post-1950 global warming. My examination begins to provide an alternative to standard, probabilistic assessments of OUR FAULT. It also brings out some of the limitations of variety of evidence considerations in assessing this and other hypotheses about the causes of climate change, and illuminates the epistemology of optimal (...) fingerprinting studies. Finally, it shows that some features of Mayo's perspective should be kept in whatever approach is preferred for assessing hypotheses about the causes of climate change. (shrink)

This paper analyzes Deborah Mayo's error-statistical (ES) account of scientific evidence in order to clarify the kinds of "material postulates" it requires and to explain how those assumptions function. A secondary aim is to explain and illustrate the importance of the security of an inference. After finding that, on the most straightforward reading of the ES account, it does not succeed in its stated aims, two remedies are considered: either relativize evidence claims or introduce stronger assumptions. The choice between these (...) approaches turns on the value attached to two aims of inquiry that are in tension: drawing strong, informative conclusions and reasoning securely. (shrink)

We provide a full characterization of computational error states for information systems. The class of errors considered is general enough to include human rational processes, logical reasoning, scientific progress and data processing in some functional programming languages. The aim is to reach a full taxonomy of error states by analysing the recovery and processing of data. We conclude by presenting machine-readable checking and resolve algorithms.