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The Nature of Scientific Evidence
Statistical, Philosophical, and Empirical Considerations
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Edited by:
Mark L. Taper
and Subhash R. Lele
Language:
English
Published/Copyright:
2004
About this book
An exploration of the statistical foundations of scientific inference, The Nature of Scientific Evidence asks what constitutes scientific evidence and whether scientific evidence can be quantified statistically. Mark Taper, Subhash Lele, and an esteemed group of contributors explore the relationships among hypotheses, models, data, and inference on which scientific progress rests in an attempt to develop a new quantitative framework for evidence. Informed by interdisciplinary discussions among scientists, philosophers, and statisticians, they propose a new "evidential" approach, which may be more in keeping with the scientific method. The Nature of Scientific Evidence persuasively argues that all scientists should care more about the fine points of statistical philosophy because therein lies the connection between theory and data.
Though the book uses ecology as an exemplary science, the interdisciplinary evaluation of the use of statistics in empirical research will be of interest to any reader engaged in the quantification and evaluation of data.
Though the book uses ecology as an exemplary science, the interdisciplinary evaluation of the use of statistics in empirical research will be of interest to any reader engaged in the quantification and evaluation of data.
Author / Editor information
Mark L. Taper is an associate professor in the Department of Ecology at Montana State University. Subhash R. Lele is a professor in the Department of Mathematics and Statistical Sciences at the University of Alberta.
Reviews
"The book is a rare find: a source that could be used in graduate seminars in statistics, philosophy, or biology....It is brimming with ideas....It deserves a read by everyone."
— Marc Mangel, Science"This is a challenging, stimulating, and important book Although some of the chapters are not for the statistically naive, all are thorough and provocative....The Nature of Scientific Evidence should be read by all ecologists who interpret data as evidence for or against specific hypotheses."— Gerry Quinn, Trends in Ecology and Evolution
"The Nature of Scientific Evidence may well be viewed as a landmark publication in years to come, one that was the precursor to a new set of statistical methodologies based on evidence and likelihood. . . . We unreservedly recommend it to every ecologist wanting to understand more about the relationship between logic, evidence, analysis and inference – which, after all, constitutes the essence of the scientific method."
— Graeme Hastwell and S. Raghu, Austral Ecology"The book is important not because of its specific content, but because of what it represents: a cross-disciplinary dialogue that addresses key issues in data analysis."
— Nicholas J. Gotelli, Ecoscience"It is precisely because this nicely and carefully edited book will provide more questions than answers that it deserves to be read and discussed by the wide audience of statisticians, philosophers of science, and scientists to whom it addresses the important problem of the evaluation of scientific evidence."
— Pablo Inchausti, Quarterly Review of Biology"This volume is a wonderful guide helping ecologists to understand many of the statistical nuances as well as an introduction to some deep-rooted methodological and philosophical issues in data analysis. . . . An important and necessary discussion that ecologists need to have."
— Marc W. Cadotte, Biodiversity ConservationTopics
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Frontmatter
i -
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Contents
vii -
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Foreword
xi -
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Preface
xv - Part 1. Scientific Process. Overview
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1. A Brief Tour of Statistical Concepts
3 - 2. Models of Scientific Inquiry and Statistical Practice: Implications for the Structure of Scientific Knowledge
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2.1 Commentary
32 -
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2.2 Commentary
39 -
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2.3 Rejoinder
43 - 3. Experiments, Observations, and Other Kinds of Evidence
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3.1 Commentary
66 -
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3.2 Commentary
67 -
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3.3 Rejoinder
69 - Part 2. Logics of Evidence
- 4. An Error-Statistical Philosophy of Evidence
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4.1 Commentary
97 -
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4.2 Commentary
99 -
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4.3 Rejoinder
101 - 5. The Likelihood Paradigm for Statistical Evidence
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5.1 Commentary
138 -
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5.2 Commentary
140 -
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5.3 Rejoinder
145 - 6. Why Likelihood?
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6.1 Commentary
165 -
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6.2 Commentary
167 -
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6.3 Rejoinder
181 - 7. Evidence Functions and the Optimality of the Law of Likelihood
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7.1 Commentary
203 -
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7.2 Commentary
205 -
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7.3 Rejoinder
207 - Part 3. Realities of Nature
- 8. Whole-Ecosystem Experiments: Replication and Arguing from Error
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8.1 Commentary
248 -
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8.2 Commentary
256 -
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8.3 Rejoinder
258 - 9. Dynamical Models as Paths to Evidence in Ecology
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9.1 Commentary
286 -
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9.2 Commentary
290 -
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9.3 Rejoinder
292 - 10. Constraints on Negative Relationships: Mathematical Causes and Ecological Consequences
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10.1 Commentary
308 -
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10.2 Commentary
315 -
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10.3 Rejoinder
318 - Part 4. Science, Opinion, and Evidence
- 11. Statistics and the Scientific Method in Ecology
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11.1 Commentary
360 -
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11.2 Commentary
362 -
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11.3 Rejoinder
367 - 12. Taking the Prior Seriously: Bayesian Analysis without Subjective Probability
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12.1 Commentary
400 -
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12.2 Rejoinder
401 - 13. Elicit Data, Not Prior: On Using Expert Opinion in Ecological Studies
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13.1 Commentary
423 -
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13.2 Commentary
428 -
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13.3 Rejoinder
431 - Part 5. Models, Realities, and Evidence
- 14. Statistical Distances as Loss Functions in Assessing Model Adequacy
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14.1 Commentary
478 -
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14.2 Commentary
480 -
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14.3 Rejoinder
483 - 15. Model Identification from Many Candidates
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15.1 Commentary
501 -
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15.2 Commentary
508 -
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15.3 Rejoinder
519 - Part 6. Conclusion
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16. The Nature of Scientific Evidence: A Forward-Looking Synthesis
527 -
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Contributors
553 -
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Index
557
Publishing information
Pages and Images/Illustrations in book
eBook published on:
December 15, 2010
eBook ISBN:
9780226789583
Pages and Images/Illustrations in book
Main content:
448
Other:
25 line drawings, 6 tables
eBook ISBN:
9780226789583
Keywords for this book
scientific evidence; data; statistics; quantifiable; hypotheses; models; inference; methodology; inquiry; research; ecology; experiment; observations; likelihood; replication; ecosystem; science; nonfiction; opinion; subjective probability; bayesian analysis; model adequacy; expertise; scholarship; academia; technology; simulation
Audience(s) for this book
Professional and scholarly;