Information Experimental Knowledge

Introduction -- Aspects of experimentation -- Information and experimentation -- Ways of experimenting.

An ambitious new model of experimentation that will reorient our understanding of the key features of experimental practice. What is experimental knowledge, and how do we get it? While there is general agreement that experiment is a crucial source of scientific knowledge, how experiment generates that knowledge is far more contentious. In this book, philosopher of science James Mattingly explains how experiments function. Specifically, he discusses what it is about experimental practice that transforms observations of what may be very localized, particular, isolated systems into what may be global, general, integrated empirical knowledge. Mattingly argues that the purpose of experimentation is the same as the purpose of any other knowledge-generating enterprise—to change the state of information of the knower. This trivial-seeming point has a non-trivial consequence: to understand a knowledge-generating enterprise, we should follow the flow of information. Therefore, the account of experimental knowledge Mattingly provides is based on understanding how information flows in experiments: what facilitates that flow, what hinders it, and what characteristics allow it to flow from system to system, into the heads of researchers, and finally into our store of scientific knowledge.

Preface1: Learning from Error 2: Ducks, Rabbits, and Normal Science: Recasting the Kuhn's-Eye View of Popper 3: The New Experimentalism and the Bayesian Way 4: Duhem, Kuhn, and Bayes 5: Models of Experimental Inquiry 6: Severe Tests and Methodological Underdetermination7: The Experimental Basis from Which to Test Hypotheses: Brownian Motion8: Severe Tests and Novel Evidence 9: Hunting and Snooping: Understanding the Neyman-Pearson Predesignationist Stance10: Why You Cannot Be Just a Little Bit Bayesian 11: Why Pearson Rejected the Neyman-Pearson (Behavioristic) Philosophy and a Note on Objectivity in Statistics12: Error Statistics and Peircean Error Correction 13: Toward an Error-Statistical Philosophy of Science ReferencesIndex Copyright © Libri GmbH. All rights reserved.

One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.

What we know about the world and its opportunities limits what we do. If we do not know that there is a pot of gold at the end of the rainbow, we will not follow it. If we do not know that a desert cactus contains water, we will not cut into it for sustenance. Often, however, we do know things about the world and yet the knowledge does not seem to be reflected in behavior. Explaining this fact simply in terms of inadequate motivation for expression or incomplete memory for the important in formation does not really add much to our understanding. The ex pression of knowledge can be interrupted in very special ways by a variety of more specific conditions-fatigue, sources of forgetting that may include failure of memory retrieval, emotion, and various dysfunc tions of brain and body systems-that are not satisfactorily incorporated by any current theories of motivation or memory. Also, a dissociation between knowledge and its expression can take the form of applying knowledge without apparent awareness of this action, a phenomenon that requires complicated assumptions for explanation in terms of either motivation or memory. Dissociations between knowledge and action may be striking. After driving home on a familiar route we may not be able to report whether the last three traffic lights were red or green; yet we must have re sponded appropriately to them.

We may learn from our mistakes, but Deborah Mayo argues that, where experimental knowledge is concerned, we haven't begun to learn enough. Error and the Growth of Experimental Knowledge launches a vigorous critique of the subjective Bayesian view of statistical inference, and proposes Mayo's own error-statistical approach as a more robust framework for the epistemology of experiment. Mayo genuinely addresses the needs of researchers who work with statistical analysis, and simultaneously engages the basic philosophical problems of objectivity and rationality. Mayo has long argued for an account of learning from error that goes far beyond detecting logical inconsistencies. In this book, she presents her complete program for how we learn about the world by being "shrewd inquisitors of error, white gloves off." Her tough, practical approach will be important to philosophers, historians, and sociologists of science, and will be welcomed by researchers in the physical, biological, and social sciences whose work depends upon statistical analysis.

Originally published in 1974, this volume presents seven detailed views of human information processing at the time. While no single volume can do justice to the breadth of the area, it was hoped that the present selections reflected both the content and methodological approaches currently used by experimental psychologists concerned with the issues and problems of human information processing. The organization of the book is simple, proceeding from the human performance end of the continuum, an overview of which is given in the first chapter. Successive chapters are progressively more concerned with human cognition, and the last chapter gives an overview of human cognition. The intervening chapters are devoted to more specific topics and yield a detailed portrait of the models, findings, and methodology of human information processing.

The forms taken by scientific writing help to determine the very nature of science itself. In this closely reasoned study, Charles Bazerman views the changing forms of scientific writing as solutions to rhetorical problems faced by scientists arguing for their findings. Examining such works as the early Philosophical Transactions and Newton's optical writings as well as Physical Review, Bazerman views the changing forms of scientific writing as solutions to rhetorical problems faced by scientists. The rhetoric of science is, Bazerman demonstrates, an embedded part of scientific activity that interacts with other parts of scientific activity, including social structure and empirical experience. This book presents a comprehensive historical account of the rise and development of the genre, and views these forms in relation to empirical experience.