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Evidence and Evolution: The Logic Behind the Science [Paperback]

Elliott Sober

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Book Description

21 April 2008
How should the concept of evidence be understood? And how does the concept of evidence apply to the controversy about creationism as well as to work in evolutionary biology about natural selection and common ancestry? In this rich and wide-ranging book, Elliott Sober investigates general questions about probability and evidence and shows how the answers he develops to those questions apply to the specifics of evolutionary biology. Drawing on a set of fascinating examples, he analyzes whether claims about intelligent design are untestable; whether they are discredited by the fact that many adaptations are imperfect; how evidence bears on whether present species trace back to common ancestors; how hypotheses about natural selection can be tested, and many other issues. His book will interest all readers who want to understand philosophical questions about evidence and evolution, as they arise both in Darwin's work and in contemporary biological research.

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'Elliott Sober, a philosopher of science at the University of Wisconsin-Madison, has long been a leader in this school [epistemology and ethics], and his latest work, Evidence and Evolution: The Logic Behind the Science, shows why he commands our attention. He is interested in the question of evidence for theories, and he shows through a careful analysis of statistical thinking (particularly Bayesian thinking) how one can make informed decisions about claims made in biology.' Michael Ruse, American Scientist

'If one is interested in the logical foundation of evolutionary reasoning, this book need to be read.' www.roterdorn.de

'… stimulating material for a graduate seminar, especially if aimed at an interdisciplinary group of students and faculty. … There is much good food for thought here, and the book is well worth the investment of time and neural firings that it requires to get to the end of it.' Trends in Ecology and Evolution

'For anyone who is interested in increasing one's understanding of evidence and how it bears on evolutionary theory, Sober's book is the best place to begin. In fact, it is the best place to end as well. The likelihood that anyone else will be able to do a better job is slim to non-existent.' David Hull, Biosciences

'… one of the most - if not the most - in depth analyses of the relationship between statistical reasoning and evidence in evolutionary biology. Indeed, the book should be read by everyone with a serious interest in evolutionary biology, in the philosophy of biology and in scientific inference more generally. … Sober has written a remarkable and remarkably important book.' History of Philosophy of Life Sciences

Book Description

How should the concept of evidence be understood? And how does it apply to the controversy surrounding creationism, natural selection and common ancestry? Elliott Sober investigates general questions about probability and evidence and shows how the answers he develops to those questions apply to the specifics of evolutionary biology.

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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Amazon.com: 3.7 out of 5 stars  3 reviews
8 of 8 people found the following review helpful
4.0 out of 5 stars An interesting debate on the philosophical foundations of tests and model choice 29 April 2010
By xi'an - Published on Amazon.com
Evidence and Evolution: The Logic Behind the Science examines the philosophical foundations of the statistical arguments used to evaluate hypotheses in evolutionary biology, based on simple examples and likelihood ratios. The difficulty with reading the book from my/a statistician's perspective is the reluctance of the author to engage into model building and even less into parameter estimation. The first chapter nonetheless constitutes a splendid coverage of the most common statistical approaches to testing and model comparison, even though the advocation of the Akaike information criterion against Bayesian alternatives is rather forceful. The book also covers an examination of the "intelligent design" arguments against the Darwinian evolution theory, predictably if unnecessarily resorting to Popperian arguments to correctly argue that the creationist perspective fails to predict anything. The following chapters cover the more relevant issues of assessing selection versus drift and of testing for the presence of a common ancestor. While remaining a philosophy treatise, Evidence and Evolution: The Logic Behind the Science is written in a way that is accessible to laymen, if rather unusual from a statistician viewpoint, and the insight about testing issues gained from Evidence and Evolution makes it a worthwhile read. In fact, it is very well-written, with hardly any typo (the unbiasedness property of AIC is stated at the bottom of page 101 with the expectation symbol E on the wrong side of the equation, Figure 3.8c is used instead of Figure 3.7c on page 204, Figure 4.7 is used instead of Figure 4.8 on page 293, Simon Tavaré's name is always spelled Taveré, vaules rather than values is repeated four times on page 339). The style is sometimes too light and often too verbose, with an abundance of analogies that I regard as sidetracking, but this makes for an easier reading. As detailed in my ([...]) review, I have points of contentions with the philosophical views about testing in Evidence and Evolution: The Logic Behind the Science as well as about the methods exposed therein, but this does not detract from the appeal of reading the book. (The lack of completely worked out statistical hypotheses in realistic settings remains the major issue in my criticism of the book.) While the criticisms of the Bayesian paradigm are often shallow (like the one on page 97 ridiculing Bayesians drawing inference based on a single observation), there is nothing fundamentally wrong with the statistical foundations of the book. I therefore repeat my earlier recommendation in favour of Evidence and Evolution: The Logic Behind the Science, Chapters 1 and (paradoxically) 5 being the easier entries. Obviously, readers familiar with Sober's earlier papers and books will most likely find a huge overlap with those but others will gather Sober's viewpoints on the notion of testing hypotheses in a (mostly) unified perspective.
4.0 out of 5 stars Four Stars 25 Oct 2014
By John B. Sorrell - Published on Amazon.com
Very good intro chapters to evidence and epistemology
30 of 53 people found the following review helpful
3.0 out of 5 stars A Technical Jungle 18 Jun 2008
By Hande Z - Published on Amazon.com
Format:Paperback|Verified Purchase
This is a very technical book on the application of various theories of probabilities (Bayesian, likelihood theory etc) in respect of the claims of "Intelligent Design" and the theory of evolution. It is a matter of academic interest whether Elliot Sober's arguments are correct because they are couched in the technical language of symbolic logic. Insofar as one can understand the general drift, he appears to have shown that the Intelligent Design argument is founded on very weak evidence (including evidence to even support the theory). Sober then tried to apply the same formula in respect of evidence concerning the theory of evolution (and natural selection) and concluded, through a series of logical exercises, that the evidence may not be as conclusive as the evolutionary scientists would like us to believe. One of the main theses of Elliot Sober in this book is that there are many ways of ascertaining truth. He suggests that just because Intelligent Design is a flawed hypothesis that does not mean that the existence of God has not been proved. But similarly, even if evolutionary theory is flawed (as Sober also tries to show) that does not mean that God, therefore, exists. The existence or otherwise of a theistic being is a matter that is examined from many more viewpoints than just the evolutionary theory. I think that unless you are interested in formal logic, and want to test Sober's technical declarations and exercises, you may find this a difficult book to grind through.
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