Assessment of error and uncertainty is a vital component of both natural and social science. Empirical research involves dealing with all kinds of errors and uncertainties yet there is significant variance in how such results are dealt with. Contributors to Error and Uncertainty in Scientific Practice present case studies of research practices across a wide spectrum of scientific fields, including experimental physics, econometrics, environmental science, climate science, industrial engineering, measurement science and statistics. They compare methodologies and present the ingredients needed for an overarching framework applicable to all.
Introduction - Marcel Boumans and Giora Hon
1 The Lack of a Satisfactory Conceptualization of the Notion of Error in the Historiography of Science: Two Main Approaches and their Shortcomings - Bart Karstens
2 Learning from Data: The Role of Error in Statistical Modelling and Inference - Aris Spanos
3 Handling Uncertainty in Models at the Science-Policy Interface - Bruce Beck
4 Experimental Knowledge in the Face of Theoretical Error - Kent W Staley
5 Modelling Measurement: Error and Uncertainty - Alessandro Giordani and Luca Mari
6 Order and Indeterminism: The Pervasiveness of Info-Gap Uncertainty - Yakov Ben-Haim
7 Learning from Error: How Experiment Got a Life (Of its Own) - Deborah Mayo
8 Communicating on the Reliability of High-Resolution Climate-Model Predictions - Leonard A Smith and Arthur C Petersen
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