The Limits of Scientific Reasoning was first published in 1984. Minnesota Archive Editions uses digital technology to make long-unavailable books once again accessible, and are published unaltered from the original University of Minnesota Press editions. The study of human judgment and its limitations is essential to an understanding of the processes involved in the acquisition of scientific knowledge. With that end in mind, David Faust has made the first comprehensive attempt to apply recent research on human judgment to the practice of science. Drawing upon the findings of cognitive psychology, Faust maintains that human judgment is far more limited than we have tended to believe and that all individuals - scientists included—have a surprisingly restricted capacity to interpret complex information. Faust's thesis implies that scientists do not perform reasoning tasks, such as theory evaluation, as well as we assume they do, and that there are many judgments the scientist is expected to perform but cannot because of restrictions in cognitive capacity. "This is a very well-written, timely, and important book. It documents and clarifies, in a very scholarly fashion, what sociologists and psychologists of science have been flirting with for several decades—namely, inherent limitations of scientific judgment," –Michael Mahoney, Pennsylvania State University David Faust is director of psychology at Rhode Island Hospital and a faculty member of the Brown University Medical School. He is co-author of Teaching Moral Reasoning: Theory and Practice.

Competence in scientific reasoning is one of the most valued outcomes of secondary and higher education. However, there is a need for a deeper understanding of and further research into the roles of domain-general and domain-specific knowledge in such reasoning. This book explores the functions and limitations of domain-general conceptions of reasoning and argumentation, the substantial differences that exist between the disciplines, and the role of domain-specific knowledge and epistemologies. Featuring chapters and commentaries by widely cited experts in the learning sciences, educational psychology, science education, history education, and cognitive science, Scientific Reasoning and Argumentation presents new perspectives on a decades-long debate about the role of domain-specific knowledge and its contribution to the development of more general reasoning abilities.

This exploration of the scientific limits of knowledge challenges our deep-seated beliefs about our universe, our rationality, and ourselves. “A must-read for anyone studying information science.” —Publishers Weekly, starred review Many books explain what is known about the universe. This book investigates what cannot be known. Rather than exploring the amazing facts that science, mathematics, and reason have revealed to us, this work studies what science, mathematics, and reason tell us cannot be revealed. In The Outer Limits of Reason, Noson Yanofsky considers what cannot be predicted, described, or known, and what will never be understood. He discusses the limitations of computers, physics, logic, and our own intuitions about the world—including our ideas about space, time, and motion, and the complex relationship between the knower and the known. Yanofsky describes simple tasks that would take computers trillions of centuries to complete and other problems that computers can never solve: • perfectly formed English sentences that make no sense • different levels of infinity • the bizarre world of the quantum • the relevance of relativity theory • the causes of chaos theory • math problems that cannot be solved by normal means • statements that are true but cannot be proven Moving from the concrete to the abstract, from problems of everyday language to straightforward philosophical questions to the formalities of physics and mathematics, Yanofsky demonstrates a myriad of unsolvable problems and paradoxes. Exploring the various limitations of our knowledge, he shows that many of these limitations have a similar pattern and that by investigating these patterns, we can better understand the structure and limitations of reason itself. Yanofsky even attempts to look beyond the borders of reason to see what, if anything, is out there.

This is Volume III of eight in a series on the Philosophy of Logic and Mathematics. Originally published in 1948, this book portrays an outline of logic and of the methodology of the exact sciences.

Dupré warns that our understanding of human nature is being distorted by two faulty and harmful forms of pseudo-scientific thinking. He claims it is important to resist scientism - an exaggerated conception of what science can be expected to do.

Not everything that claims to be science is. UNDERSTANDING SCIENTIFIC REASONING shows you easy-to-use principles that let you distinguish good science from bad information you encounter in both textbooks and the popular media. And because it uses the same processes that scientists use (but simplified), you'll know you're getting the most reliable instruction around. You'll also learn how to reason through case studies using the same informal logic skills employed by scientists.

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.