Practical Nonparametric Statistics

by W.J. Conover

Book Overview

A self-contained introduction to the theory and methods of non-parametric statistics. Presents a review of probability theory and statistical inference and covers tests based on binomial and multinomial distributions and methods based on ranks and empirical distributions. Includes a thorough collection of statistics tables, hundreds of problems and references, detailed numerical examples for each procedure, and an instant consultant chart to guide...

• Format: Hardcover
• Language: English
• ISBN: 0471028673
• ISBN13: 9780471028673
• Release Date: September 1980
• Publisher: Wiley
• Length: 512 Pages
• Weight: 1.94 lbs.
• Dimensions: 1.2" x 6.7" x 9.3"

Customer Reviews

excellent update

Since I now have a copy of the third edition I can now say that many of my comments on the second edition still hold. The book is authoritative, clearly written and very much applications oriented. Conover has done a good job of updating it with recent developments. He provides a nice introductory treatment of bootstrap among other things.

Practical indeed!

This book is a great reference for the scientist needing to know how best to interpret their results. It has clear explanations for when to use particular tests as well as how to use the tests when multiple observers are rating the same event. Analogous parametric tests are mentioned along with comparisons (A.R.E.) of their relative efficiency. This was much better info than I got from the statisticians at my workplace - of course, the statisticians want to keep my business, not tell me how to do it!

Clear and practical

Standard statistics make assumptions about how the data are distributed, then give results based on the assumed distribution. Two big problems are that the distribution buried in the analysis may not be the right one, and that the assumption might not even be visible in the analysis. "Nonparametric statistics" (NPS) make no assumptions about the distribution. They work no matter how the data are distributed. Even better, they sometimes work to determine whether the standard techniques have any hope of giving answers.For the practitioner, this book is the broadest catalog I know of how-to for NPS: when each analysis applies and how to apply it. Even more, it gives insight into how some of the tests work. That gives the reader a better chance to understand each technique's strengths, weaknesses, and applicability. For the student, including self-taught, it's a clear and well-organized textbook. The exercises are varied and generally meaningful, and half have answers (though little discussion of how the answers were derived).I wish the book gave more background, including how some of the distributions are derived. Most times, seeing more of the derivation gives me more confidence in using an analysis. Face it, almost every real-life situation needs to be bashed a bit to fit the format expected by a test. Knowing more of the background gives me more assurance that my machinations don't break any important assumptions. Still, it's the author's choice to emphasize practice over theory and I have to respect that.More seriously, I would like to see the bootstrapping section enlarged. Many modern applications, particularly in biology, deal with data so complex that they define analysis or even real understanding. Bootstrapping is just one of many randomization and resampling techniques used for such data. More discussion on the design and analysis of resampling techniques would have been very useful.The book meets its goals, though, and does so admirably. I'm not a stat specialist, but this is the book I'll recommend for heavy users who want a little more than rote recitation of analytic techniques.

Excellent Introduction

This is a very impressive book. All concepts are introduced in an elementary fashion, with derivation following only after an example of the technique. The explanations are lucid and the extensive lists of references very helpful. I would heartily recommend this book to anyone interested in robust estimators and nonparametric methods.

One of the Best Nonparametric Books I am Aware Of

I am involved with environmental statistics software development. When I need to look up something related to nonparametric statistics I find Conover is the first place I turn to. It is written well, does not require that the user have advanced mathematical skills, and contains an extensive list of references. My use is limited to the 2nd edition, but I would guess the 3rd edition is only better.