Measuring and Reasoning
Numerical Inference in the Sciences
£48.99
- Author: Fred L. Bookstein, University of Washington and Universität Wien, Austria
- Date Published: May 2014
- availability: In stock
- format: Hardback
- isbn: 9781107024151
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In Measuring and Reasoning, Fred L. Bookstein examines the way ordinary arithmetic and numerical patterns are translated into scientific understanding, showing how the process relies on two carefully managed forms of argument: • Abduction: the generation of new hypotheses to accord with findings that were surprising on previous hypotheses, and • Consilience: the confirmation of numerical pattern claims by analogous findings at other levels of measurement. These profound principles include an understanding of the role of arithmetic and, more importantly, of how numerical patterns found in one study can relate to numbers found in others. More than 200 figures and diagrams illuminate the text. The book can be read with profit by any student of the empirical nature or social sciences and by anyone concerned with how scientists persuade those of us who are not scientists why we should credit the most important claims about scientific facts or theories.
Read more- Includes two hundred pictures and diagrams that convey the essence of the subject matter without requiring any mastery of the underlying theories
- Features dozens of worked examples, many of historical importance, capturing the essence of the most powerful tools for quantitative reasoning across the natural and social sciences
- Explanations of the logic and rhetoric of the bell curve, regression and correlation, and likelihood tests as they drive today's best examples of scientific reasoning
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×Product details
- Date Published: May 2014
- format: Hardback
- isbn: 9781107024151
- length: 559 pages
- dimensions: 235 x 160 x 34 mm
- weight: 0.89kg
- contains: 180 b/w illus.
- availability: In stock
Table of Contents
Part I. The Basic Structure of a Numerical Inference:
1. Getting started
2. Consilience as a rhetorical strategy
3. Abduction and strong inference
Part II. A Sampler of Strategies:
4. The undergraduate course
Part III. Numerical Inference for General Systems:
5. Abduction and consilience in more complicated systems
6. The singular value decomposition: a family of pattern engines for organized systems
7. Morphometrics, and other examples
Part IV. What Is to Be Done?:
8. Retrospect and prospect.
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