An enormous intellectual adventure. In this groundbreaking new book, the American biologist Edward O. Wilson, considered to be one of the world's greatest living scientists, argues for the fundamental unity of all knowledge and the need to search for consilience --the proof that everything in our world is organized in terms of a small number of fundamental natural laws that comprise the principles underlying every branch of learning. Professor Wilson, the pioneer of sociobiology and biodiversity, now once again breaks out (...) of the conventions of current thinking. He shows how and why our explosive rise in intellectual mastery of the truths of our universe has its roots in the ancient Greek concept of an intrinsic orderliness that governs our cosmos and the human species--a vision that found its apogee in the Age of Enlightenment, then gradually was lost in the increasing fragmentation and specialization of knowledge in the last two centuries. Drawing on the physical sciences and biology, anthropology, psychology, religion, philosophy, and the arts, Professor Wilson shows why the goals of the original Enlightenment are surging back to life, why they are reappearing on the very frontiers of science and humanistic scholarship, and how they are beginning to sketch themselves as the blueprint of our world as it most profoundly, elegantly, and excitingly is. (shrink)

"This is a wonderful book! In "How Scientists Explain Disease," Paul Thagard offers us a delightful essay combining science, its history, philosophy, and sociology.

This book is a cultural phenomenology of doing physics. It describes the ways physicists actually do their work--their motives, and their ways of making sense of the world--so that outsiders can understand it. Martin H. Krieger explains that physicists employ a small number of everyday notions to get at the world experimentally and conceptually.

It is far from obvious that outside of highly specialized domains such as commercial agriculture, the methodology of biometrics—quantitative comparisons over groups of organisms—should be of any use in today’s bioinformatically informed biological sciences. The methods in our biometric textbooks, such as regressions and principal components analysis, make assumptions of homogeneity that are incompatible with current understandings of the origins of developmental or evolutionary data in historically contingent processes, processes that might have come out otherwise; the appropriate statistical methods are (...) those suited to random walks, not normal distributions. A valid methodology would further require that especially close attention be paid to the difference between aspects of processes that are plastic, those that encode their own histories or biographies, and the very small fraction of quantifications that can usefully and realistically be modeled as varying by colored noise around a central tendency that itself has some quantitative meaning. This point of view—that only a vanishingly small fraction of the quantitative information borne by any living organism is worth quantifying—is illustrated by some data on a human birth defect, namely, fetal alcohol syndrome. In a suggestive metaphor, the biometrician is like the pilgrim in Friedrich’s painting Der Wanderer über dem Nebelmeer, uncertain as to whether to measure the mountains or the clouds. The mountains stand for contingent history, the clouds for the subset of the data most closely matching controlled experiments suitable for quantitative biometric summary. Biometrics applies to the clouds, not the mountains. The success of statistical methods comes at the expense of all the theories that we simultaneously hold to be true about the biological materials to which they both pertain. Biometrics is thus complementary to all of the emerging reductionist sciences of biological structure. (shrink)

Biometrics has done damage with levels of R or p or Student’s t. The damage widened with Ronald A. Fisher’s victory in the 1920s and 1930s in devising mechanical methods of “testing,” against methods of common sense and scientific impact, “oomph.” The scale along which one would measure oomph is particularly clear in biomedical sciences: life or death. Cardiovascular epidemiology, to take one example, combines with gusto the “fallacy of the transposed conditional” and what we call the “sizeless stare” of (...) statistical significance. Some medical editors have battled against the 5% philosophy, as did, for example, Kenneth Rothman, the founder of Epidemiology. And decades ago a sensible few in education, ecology, and sociology initiated a “significance test controversy.” But, grantors, journal referees, and tenure committees in the statistical sciences had faith that probability spaces can substitute for scientific judgment. A finding of p <.05 is deemed to be “better” for variable X than p <.11 for variable Y. It is not. It depends on the oomph of X and Y—the effect size, size judged in the light of how much it matters for scientific or clinical purposes. In 1995 a Cancer Trialists’ Collaborative Group, for example, came to a rare consensus on effect size: 10 different studies had agreed that a certain drug for treating prostate cancer can increase patient survival by 12%. An 11th study published in the New England Journal in 1998 dismissed the drug. The dismissal was based on a t-test, not on what William Gosset had called, against Ronald A. Fisher’s machinery, “real” error. (shrink)

First published in 2000. Routledge is an imprint of Taylor & Francis, an informa company.