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Statistics in a Nutshell: A Desktop Quick Reference
Need to learn statistics for your job? Want help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference for anyone new to the subject. Thoroughly revised and expanded, this edition helps you gain a solid understanding of statistics without the numbing complexity of many college texts.Each chapter presents easy-to-follow descriptions, along with graphics, formulas, solved examples, and hands-on exercises. If you want to perform common statistical analyses and learn a wide range of techniques without getting in over your head, this is your book.Learn basic concepts of measurement and probability theory, data management, and research designDiscover basic statistical procedures, including correlation, the t-test, the chi-square and Fisher's exact tests, and techniques for analyzing nonparametric dataLearn advanced techniques based on the general linear model, including ANOVA, ANCOVA, multiple linear regression, and logistic regressionUse and interpret statistics for business and quality improvement, medical and public health, and education and psychologyCommunicate with statistics and critique statistical information presented by others
594 pages, ebook
First published July 25, 2008
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Displaying 1 - 23 of 23 reviews
August 4, 2011
Great, concise, practical reference on common statistics. I picked this up for a brief refresher. Nice to have on the bookshelf and reference when I need.
December 31, 2012
One of the biggest problems faced in teaching statistics is the gap between learning the methods to actually using them. Statistics classes that are based on learning formulas fail due to the disconnect between learning formulas and the reality that very rarely are these methods used by implementing the formulas that are so painstakingly taught. But learning statistics as a set of steps or functions in a computer package only gets a little further. The real goal should be what methods should be used and why. The how is almost secondary. Statistics in a Nutshell focuses on the what and the why. I would not use this to learn how to perform a technique or its formulas, but this is where to go to understanding how the various methods of statistical analysis should be used and their qualities. It is meant to be read, not just studied, and as such it holds a different place than other statistics texts.
When I first learned statistics, the focus was on learning formulas that calculated various values. But the problems I could work on were only toys, and it took so long that we did not get into much of understanding what we were dong. Now with readily available software packages, the temptation is to focus on the mechanics of implementing a procedure on a set of data and reading the computer output. But software documentation and even books that teach statistics fall into the trap of focusing on how a method works then applying it and not as much on why. Part of this is because of the pressure of having to cover topics, but the fact that the methods are presented in isolation, without their application context so it is rare to grapple with the question of how to know what needs to be done and instead the focus is on how to do it.
Statistics in a Nutshell is the other kind of book. I was taught that for computer programming for any language you wanted a book that focused on methods, but also a book that focused on morals, the why you use a language feature. This is the morals book for statistical programming. You read it, not to learn how to calculate statistical output or implement visualizations, but to think about what method or visualization is appropriate to help understand the data and environment and to communicate those truths to an audience.
Because of the expectation that any course that teaches statistics gives the students a toolkit, this would never be a good book for teaching a course. But in the real world, what is more important is that you understand what these statistical methods are and why you use one over the other. So for the data analyst or a student who needs an overview of everything this is ideal. It would also be ideal for someone who may not have the time to go into detailed study of statistical methods, but needs to interpret the results or work with statisticians and data analysts. This book will help interpret what you get and ask the right questions to both understand statistical results and perhaps point those who are doing the analysis in the right direction so that they are answering the right questions.
Note: I received a free electronic copy of this book through the O'Reilly Press Bloggers program.
When I first learned statistics, the focus was on learning formulas that calculated various values. But the problems I could work on were only toys, and it took so long that we did not get into much of understanding what we were dong. Now with readily available software packages, the temptation is to focus on the mechanics of implementing a procedure on a set of data and reading the computer output. But software documentation and even books that teach statistics fall into the trap of focusing on how a method works then applying it and not as much on why. Part of this is because of the pressure of having to cover topics, but the fact that the methods are presented in isolation, without their application context so it is rare to grapple with the question of how to know what needs to be done and instead the focus is on how to do it.
Statistics in a Nutshell is the other kind of book. I was taught that for computer programming for any language you wanted a book that focused on methods, but also a book that focused on morals, the why you use a language feature. This is the morals book for statistical programming. You read it, not to learn how to calculate statistical output or implement visualizations, but to think about what method or visualization is appropriate to help understand the data and environment and to communicate those truths to an audience.
Because of the expectation that any course that teaches statistics gives the students a toolkit, this would never be a good book for teaching a course. But in the real world, what is more important is that you understand what these statistical methods are and why you use one over the other. So for the data analyst or a student who needs an overview of everything this is ideal. It would also be ideal for someone who may not have the time to go into detailed study of statistical methods, but needs to interpret the results or work with statisticians and data analysts. This book will help interpret what you get and ask the right questions to both understand statistical results and perhaps point those who are doing the analysis in the right direction so that they are answering the right questions.
Note: I received a free electronic copy of this book through the O'Reilly Press Bloggers program.
April 12, 2014
I got through 150 pages before giving up. I have the second edition, which was supposed to fix all the mistakes from the first, but there are still problems. The book is still poorly organized: early chapters advise you to refer to later chapters before proceeding. The book is heavy on formulas and jargon and light on explaining the concepts behind statistics. Might be a good reference if you took a good class ten years ago and need a refresher, but the material in this book doesn't stand on its own.
September 14, 2020
The tone was very dry making the material a complete slog to get through. The author repeats some concepts in multiple chapters. That said, the book serves as a very good reference manual providing information on the various statistical techniques used in various scenarios and situations.
March 28, 2015
This book surveys commonly used statistics, with an emphasize on how to use them. It covers basic inferential statistics (e.g., t-test, correlation, ANOVA) but also includes specific sections for statistics for specific fields such education, health, etc.
I liked the fact that the author always tries to convey an interpretation for the values that are computed. Yet, I wish the logic behind the formulae be sometimes further detailed (e.g., ANOVA).
To my opinion, this is a good reference for those who just have to do statistics from time to time.
I liked the fact that the author always tries to convey an interpretation for the values that are computed. Yet, I wish the logic behind the formulae be sometimes further detailed (e.g., ANOVA).
To my opinion, this is a good reference for those who just have to do statistics from time to time.
January 8, 2019
My copy of this book is well-loved, with scotch-tape repairs and coffee stains.
This is my go-to book when starting on any research or analysis project. It's far from exhaustive, but what it does, it does well. This is a great book for getting an quick, accessible explanation of a concept to springboard into more complex topics. I find myself repeatedly dusting it off to remind myself of class material from years ago that's gone a bit fuzzy, so that I can get back into making use of it.
This is my go-to book when starting on any research or analysis project. It's far from exhaustive, but what it does, it does well. This is a great book for getting an quick, accessible explanation of a concept to springboard into more complex topics. I find myself repeatedly dusting it off to remind myself of class material from years ago that's gone a bit fuzzy, so that I can get back into making use of it.
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July 25, 2020Excellent reference. I'm not sure I read the entire thing because I looked stuff up as needed but I often ended up reading further because it's just…interesting.
June 16, 2022
It is my favorite statistical book that has accompanied me for long-night days.
July 23, 2023
Online courses and many books on data science teach you how to apply statistical methods, but rarely would they explain when to choose which method, and their respective pros and cons. Statistics in a Nutshell, however, introduces the common methods out there, explains their purposes, and helps me understand the rationale for choosing the appropriate model.
I read this book with CharGPT as my "tutor" to give me use cases, explain the concept in different ways, and check my understanding. It adds a lot of depth to the book's content, which it sometimes lacks. So you may try this way too.
I read this book with CharGPT as my "tutor" to give me use cases, explain the concept in different ways, and check my understanding. It adds a lot of depth to the book's content, which it sometimes lacks. So you may try this way too.
May 27, 2019
Glad to refresh my memories with this easily readable book. This book teaches you to understand the theory behind the statistics rather than relying heavily on formulas and technicality.
January 5, 2022
Good reference material.
February 27, 2019
The best book about statistics I've ever tried to read. Unfortunately, the russian translation and edition is far from perfect: many mistakes, not only in spelling.
February 16, 2015
This is a wonderful desk-reference book to be consulted often. Especially when working with statistical computing (i.e. in R or Python), it's great to look back and read the mathematical details of what you are actually doing. Those details are often expressed in a work-flow and applied context, which I think is what makes this book a wonderful reference above all.
September 24, 2010
Good overview of the basics of Stat's.
June 1, 2013
Excellent algebra and geometry review following comprehensive & succinct statistics textbook.
August 23, 2015
Good introduction book to Statistics. Concise!
May 4, 2010
Over my head for daily use, and contains a couple errors that need to be fixed, but a great pocket-sized resource for reminding foggy brains.
May 2, 2012
Great for practical hands-on learning without all the background theory. It lets you get the job done.
October 3, 2011
Stats are fun
November 16, 2013
Seems this book is about everything in statistics. Sarah covered very wide areas and book looks quite overloaded by terms and formulas. It is good reference.
October 29, 2011
Great brush up, and good to have around as a refrence
March 25, 2018
Good review.
April 29, 2018
This book is worth reading just for Appendix A's review of math.
Displaying 1 - 23 of 23 reviews