Comments about oreilly Statistics in a Nutshell:

The book makes it difficult to grasp new concepts because of the errors. Reading requires two-hands: one for the highlighter and the other for the Errata sheet. The Errata sheet has 12 pages, so you can't fold it and slip it into the book. Additionally, if you print the Errata sheet, the formatting is messed up. Text are on top of other text, making it unreadable in certain spots.

Comments about oreilly Statistics in a Nutshell:

Disappointing, does not reflect well on O'Reilly. I'll have to recycle it - I wouldn't want someone to pick it up and try to learn basic statistics from it!

Comments about oreilly Statistics in a Nutshell:

I've been reading through the book as a quick review of basic statistics while trying to learn some statistics computing packages. So far, the book has been riddled with computational errors, basic errors for variable definitions, and figures that say they will show something they don't. For example, trying to define the binomial distribution, the authors confuse the variables for trials and successes in a Bernoulli process. The authors do not explain how to really calculate a t-test. For example, the authors provide a value for the mean in an example of the t-test, and then use a different mean later in the example for calculating confidence intervals without actually defining what numbers should be going into the confidence interval calculation. Not at all proof read, as another reviewer has stated.

Without a reasonable strong statistics background for a masters degree in epidemiology, I would be left confused by a great deal of the explanations in this book. Not a tried and true reference...

Comments about oreilly Statistics in a Nutshell:

I bought this hoping for the one 'magic' stats book that explained statistics in plain english. Unfortunately this is not the one. I concur that this book is riddled with errors, and it's written not so much as a reference book, but as an explanatory text. Very few worked examples. Bummer.

Comments about oreilly Statistics in a Nutshell:

I had high hopes for this book. The world really needs a reference guide for those of us who have had exposure to statistics in college, but often don't remember which tool to turn to (t-test? F-test? Chi-square? a non-parametric test? I don't remember which to use when!). It would have been perfect to provide handy reference tables for which tools to look into in various circumstances, along with the assumptions that must be met to use that tool and a reference to a page number where the tool is briefly explained with some examples. I would have been happy to use this to get me started and then google for more detailed information.

This isn't that book. There are no handy reference tables (of any sort) in this book.

Despite the "Nutshell" title, this book is written as if it is a linear, beginner primer on statistics.

I could be OK with that if it were a good beginner primer on statistics. Instead, the authors seem to assume that "Nutshell" in the title is a license to cut corners in their explanations, leaving the beginning reader confused.

The worst part is that the book is riddled with errors. If you're trying to follow the examples and learn from them, you're never sure if you just don't understand or if this is yet another typo/error. Chapter 8 on t-test is so full of computation errors and typos that I doubt it was ever proof-read.

Comments about oreilly Statistics in a Nutshell:

While many of the "Nutshell" books assume you already know the subject and simply need a reference book, Statistics In A Nutshell takes a decidedly different approach. While this book assumes you understand basic math concepts, it does not assume you have any prior background in statistics. It then proceeds to cover nearly every fundamental concept taught during an introductory statistics course.

While many introductory statistics courses go through basic descriptive statistics and move through more advanced concepts like ANOVA or liner regression, I found that this book also covered such concepts as non-parametric tests, design of experiments, and the general linear model. Certainly, these concepts are not covered in as much depth as say a college-level courses dedicated to non-parametric statistics. However, the concepts are there, and the authors provide enough information to make the discussion valuable.

The last few sections of the book discuss the use of statistics in a variety of professions, including manufacturing, business, medical, and education fields. Individuals who may not feel comfortable with their math skills can take comfort in the fact that the book provides a section on basic math skills, however, those who may be mathematically challenged may argue with the term "basic".

All in all, I think this is an excellent book for individuals who are looking to implement the scientific method and statistics in their business. The author provides sound explanations of the concepts and plenty of figures and tables to explain difficult concepts. I'd highly recommend this book for the reader who is not afraid of a little math and wants to understand statistics and statistical concepts.