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.
This exploration of empirical inference presents descriptions of the processes by which scientific measurements support explanations of our world.
Fred L. Bookstein is Professor of Statistics at the University of Washington, Seattle; Professor of Morphometrics, Faculty of Life Sciences, University of Vienna, Austria; and an emeritus Distinguished Research Professor at the University of Michigan. Since 1977 he has produced some 300 books, chapters, articles, and videotapes on various aspects of these methods and their applications in studies of normal and abnormal craniofacial growth in humans and other mammals, studies in the neuroanatomy and behavior of schizophrenia and fetal alcohol spectrum disorders, and evolutionary studies of hominids and ammonoids. He is especially interested in how statistical diagrams can convey the valid numerical patterns that characterize complicated systems like continental drift or fetal alcohol brain damage to the broad modern public. The figures in this book include many of his current favorites along these lines.