As someone that has both interest in and fear of political methodology, I find Unifying Political Methodology an accessible reference work for graduate students. A methodology text by this author can be a little intimidating, but King shows he is more than the foremost methodologist in the discipline, but a great teacher as well. Unifying Political Methodology offers a progressive step in advancing the way political scientists communicate with each other. Ignore it at your peril.

David Salsburg is a statistician with a good knowledge of the foundations of statistics as well as being a practical statistician with a great deal of clinical trials experience. In Chapter 1 he discusses randomized controlled clinical trials. Chapters 2-5 deal with foundational issues in probability and statistics. It leads to the conclusion that hypothesis testing is appropriate for clinical trials and statistical power against alternatives is an important concept for choosing appropriate test statistics but (1)for most practical problems uniformly most powerful tests do not exist and (2)powerful tests can only be found if one is able to meaningfully restrict the class of alternative hypotheses being considered.

In Chapter 6, Salsburg makes the case for restricted tests by providing a real clinical example. Special methods are then covered in the remaining chapters. Chapter 8 deals with resampling approaches including permutation methods and the bootstrap. In Chapter 10, Neyman's often neglected theory of restricted chi-squared tests, is presented.

This is a well written and unusual book that covers methodology not seen in very many biostatistics books. However, these techniques are very relevant to the clinical trials commonly conducted at pharmaceutical companies. It is an important reference source for biostatisticians.

Those interested in statistical theory and its foundational issues will find clear and concise coverage in the first 5 chapters. However if you want more, take a look at Salsburg's new book "The Lady Tasting Tea" which just came out in 2001. In that book he raises all the same issues and more in the context of discussing the great statisticians of the 20th Century.

I use this book as a companion to the comnprehensive text in my graduate course in Applied Business Statistics. It is a major benefit to those students who have been away from the subject for a significant period of time. Plainly written with no jargon, it introduces the basics of various statistical procedures extremely well.

This book covers the history and application of one of the most important, but underutilized statistical methods for engineers. Variance components will give you insight unmatched by the typical methods of analysis currently in use. The explanations are readable by engineers with one to two years of statistical methods experience. This is a must have for semiconductor(any?) manufacturing process or product engineer.

Though the book never really mentions neuro-linguistic programming, some of the techniques are basically the same. This has been found to be a very effective technique for people to improve every area of their lives. Just knowing that you have more control over your life than you give yourself credit for can make a huge difference in the quality of your life. So much really depends on you!

This book is for people who want to learn statistics by conducting experimentations.
With the aid of my notebook, I really enjoyed having a portable statistical laboratory which enhanced my understanding to some difficult statistical theories. The examples also enrich the understanding as well as provide a practical dimension to most of statistical concepts.

I've struggled with statistics for years, and had resigned myself to continuing that struggle until I read this wonderful book. Where most books assume that you remember lessons from high school this one starts from scratch. It also differs from other books by teaching you how statistics work instead of force feeding you formulas that you learn by rote, but do not impart an understanding of how statistics work.

I like the way that this book uses illustrations and clearly describes the 'whys' to make statistics come alive. Shortly after I started reading this book (which is actually interesting!), I began seeing the significance of data distributions, relationships and dependencies. This not only will improve your understanding of statistics, but also gives you the confidence to tackle problems that may have intimidated you or were beyond your knowledge level.

If you need to quickly refresh your knowledge and skills, or want to understand statistics instead of crunching formulas, this book is a fast way to get there.

It's a beautifully written book. Many useful techniques with examples of application. Great reference book. Must have in your library.

This is a delightful introduction to probability and mathematical statistics. There are already quite a few nice probability texts at this level (Jim Pitman's PROBABILITY is a favorite of mine) and many serviceable introductions to mathematical statistics (Rice's MATHEMATICAL STATISTICS AND DATA ANALYSIS and Cassella & Berger's STATISTICAL INFERENCE are good examples), but Williams is very different from these. He manages to teach a lot of beautiful probability along with some practical statistics. The approach is thoroughly modern (including in depth coverage of modern Bayesian methods) and reasonably applied (much more applied than you would expect from his other books). He is opinionated, but never boring.

The only negatives I can think of are (1) too few problems, especially easy problems; (2) quite a few typos--it's a first printing; and (3) some asides, especially on measure theoretic issues, that the target audience may find more confusing than helpful.

I wish this book had been around when I first studied this material.

This was a stimulating and interesting book which helped to review the whole topic as applied to the social sciences. This would of use to both undergraduate and informative to graduates who may not be aware of the arguments within it.