I have taught an introductory statistics class for psychology majors at a big-10 school more often than I care to remember, using this edition of Moore and McCabe.
Although the book has a number of shortcomings (more on those later), it also has its advantages:
1) Given the age and popularity of this text, it can be easily and cheaply bought used.
2) This is book written at a level that is accessible to undergraduate students, even those whose mathematics background is lacking
3) The layout of the book is clear (unlike the next edition, the problems are not scattered all over the chapter). The review at the end of each section is helpful for students
4) Each section has a large variety of problems, both problems that can be solved by hand and problems that are best solved using software (data sets are usually included on the student CD-ROM)
5) The "extra" chapters on the CD-ROM are well-written. A honors or "advanced" course in introductory undergraduate statistics can easily be based on all the chapters in the (hardcopy) textbook and a chapter of one's own choice (e.g. Logistic Regression) from the CD.
6) The CD-ROM includes Power Point slides which can be used by students who prefer this format to my lectures (which are chalkboard-based)
7) A number of companion books, the most useful being the Excel and SPSS manuals are available.
8) A course website package for the compass system exists. This can provide weekly online multiple-choice quizzes, grade reporting features as well as an easy way to share files with students.
Some disadvantages of this textbook are:
1) Some data sets that should be on the student CD are mysteriously missing
2) The EESEE database with case studies' interface is very badly designed; some data sets are missing; students find it hard to import the existing data sets into statistical software.
3) Most formulae are simply stated without any proof. While this does alleviate the fears of some of the less-than-gifted students, it makes the course less enlightening for students who come into the course adequately prepared and motivated.
4) The books' focus on the standard deviation over the variance is sometimes a little extreme, especially in the section on the rules of Expectations and Variances.
5) The section on expectations and variances should be split into separate sections for continuous and discrete random variables; this might help some of the more confused students.
6) The chapter on 1-way ANOVA does not include the expectations of the mean squares or any information on the regression model for ANOVA. Furthermore, Bonferroni is the only method of correcting for type-I error that is discussed. Others could have at least been listed.
7) The chapter on 2-way ANOVA should be expanded. The authors do not mention that they are talking solely about fixed-effects 2-way ANOVA, do not provided the formulae for the mean squares (or their expectations) and do not explain 2-way ANOVA as a multiple regression model. This is somewhat mitigated by the fact that a regular class (i.e. not an honours class) will probably not be able to cover all 12 chapters prior to the 2-way ANOVA chapter (and thus this chapter is not used either).
I have investigated the possibility of using a different textbook, but have not found a replacement that has the strengths of this book without the weaknesses.
I would recommend this book for an elementary introduction to statistics for undergraduates in the social sciences with the caveat that the instructor will have to supplement certain sections for those students who are intelligent, well-prepared and motivated.

There are many statistics books out there, but this one really stands out. The style is extremely clear, the examples pertinent and the author constantly brings the reader to the basics, no non-sense, showing why and how particular items are important. Practitioners will find this book especially useful since it deals mainly with statistical inference (tests) and gives a great amount of real life examples.

The experienced statistician should stay away (if looking for an advanced textbook) since this book does not go into deep mathematical or theoretical details (the necessary minimum is provided).

A quick peek at the toc gives a reliable idea of its content.

I ordered this book for a summer school class, and it was not even sent out until three weeks after I ordered it. I received the book five weeks after I orginally ordered it, and two weeks too late for my class. I was and still am very annoyed by the seller.

I never understood why this textbook has enjoyed such enduring popularity. This book presents statistics as nothing more than one formula after another, rarely stopping to justify any of them, and when it does try to explain something, it inevitably resorts to long and convoluted exposition. This is my main gripe with this textbook -- there is too much text and not enough math. I realize that this book is not concerned with the theoretical aspects of statistics, but in order to understand and apply statistics at even the most basic level, a certain facility with math is necessary. This book does its utmost to strip statistics of math, replacing it with long-winded verbal explanations wherever possible. Thus the book ends up being twice as long than perhaps it needs to be. (I've seen other introductory stat textbooks that cover everything in this textbook and more in 500 pages or less.)

I realize that many students taking introductory statistics are averse to math. But by replacing the math with exposition, this book does statistics a great disservice. Many people enjoy reading long-winded 1000-page novels because there is often good prose and a good story to be had; long-winded 1000-page statistics textbooks, on the other hand, are a chore for anybody to read because -- let's face it -- it just isn't possible to talk about sampling distributions like Tolstoy tells Anna Karenina. (Disclaimer: Sampling distributions can be every bit as exciting as Tolstoy, but it usually isn't much fun to read about them!) When books like these try to teach statistics as if it were a narrative, is it any wonder that so many students claim to find statistics boring?

One thing that this book does well is deceive readers into thinking that they know statistics, but it only breeds more confusion and misconceptions that will hurt students if they ever take another statistics class. For example, the book presents the concept of normal distributions before it even mentions probability. I suppose students enjoy seeing this topic early in the course because they've heard of the normal distribution and associate statistics with it. But the normal distribution is fundamentally a phenomenon of probability, and it applies to data only insofar as it is one of the many probability models for distribution of a given set of data. Yet students are asked to find normal probabilities before they are even introduced to what probability is. The statistics department at my school has griped that the students don't understand the difference between probability distribution and distribution of a set of data. Maybe students wouldn't have this problem if the intro classes didn't use a textbook that blurs the distinction between the two! In my own experience teaching with this text, I have encountered another fundamental misunderstanding that stems from introducing normal distributions so early on -- some students start calling ANY bell-shaped and symmetric distribution "normal."

As if that were not enough, the book neglects to mention one of the most important distinctions in all of probability/statistics -- that of discrete vs. continuous random variables. How are students supposed to understand what it means for a random variable to be normally distributed when everything they have studied about random variables has been discrete?

To compound the lousy presentation of material, the authors have somehow managed to come up with the least interesting problems possible for the exercises. Whereas most statistics textbooks select their data sets from actual experiments and studies, it really seems in some of the exercises that the authors fabricated a bunch of numbers for students to punch into their calculator/MiniTab.

If you are a professor or AP Stats teacher, please do your students a favor and look elsewhere for a suitable textbook. In my experience, McClave/Sincich treats statistics correctly (with separate chapters for discrete and continuous random variables!) and has interesting problems to boot. But my experience with statistics textbooks is limited, and there may be better books out there. Shop around, but chances are that you won't find a worse textbook than this one. If you are a student, you have my sympathies. Good luck working with this textbook, and don't feel bad if statistics makes less sense after reading this textbook than before.

I never understood why this textbook is so popular. It is basically a catalog of formulas with endless exposition thrown in between. I understand that this book was not meant to be mathematical, but I cannot comprehend why anyone would enjoy being given a completely arbitrary formula without any sort of justification whatsoever -- mathematical or otherwise. I'm not talking about long and involved derivations that would require calculus (as, admittedly, many results in statistics do) -- I'm talking about simple derivations that middle-schoolers could understand that would clarify concepts that have traditionally confused students. Instead of having students parrot the precise statement for interpreting r^2, why not show them where this interpretation comes from and let them frame the statement as they understand it? Instead of pulling out your hairs every time students write "There is a 95% probability..." when trying to interpret a confidence interval, why not show them how probability fits into the theory of confidence intervals and use a little algebra to show why this interpretation is wrong (from a frequentist perspective, anyway)? These are all derivations that anyone who has taken algebra should be able to follow, and if this is too much math for the students, maybe it wouldn't be such a bad idea to have them review how to square numbers and solve inequalities.

The book instead substitutes mathematics with prose -- a lot of it. What might have been explained with a single mathematical argument instead takes several pages to explain. I found this book to be basically unreadable because of the excessive exposition, and I am impressed that there are high school students who liked this book (which, I would assume, implies that they actually read it). The multiple regression chapter was especially insufferable. Nowhere does it actually explain how to select variables for a multiple regression; instead it spends several pages of text explaining what multiple regression is. After reading the chapter, I probably knew about as much as I did before I read the chapter, and I still didn't know how to do a multiple regression.

I might be able to the overlook the above shortcomings of this book if only the problems were interesting, but the authors have somehow managed to choose the most uninteresting data sets possible. Whereas most statistics textbooks select data from actual experiments and studies, it seems that these authors merely fabricated a bunch of numbers to serve as data sets for some of the exercises.

I cannot recommend IPS as a textbook for any statistics class at any level. By replacing math with prose, it has only rendered statistics more convoluted. Get a statistics textbook that does just enough math to enlighten the theory (but not too much), and that has interesting and meaningful data sets to boot.

David Moore is a Professor of Statistics at Purdue University. He is both a great teacjer and a scholar. In addition to one or two well-written advanced books he has written a number of high quality introductory statistics books and has led the movement toward AP statistics in the high schools and active learning methods for tesching statistics at the elementary, middle and high school levels as well as at the universities.

This book is a new edition of his highly successful introductory text. This is only slightly more advanced than the text "The Basic Practice of Statistics" also written by Moore. This edition can only be as good or better than the one U have read. The inclusion of a CD for use in the course can only be a significant edition to this applied text.

I'll grant you that this is my first time in any kind of statistics class so perhaps my complaint should be leveled more at statistics in general rather than at this particular book. My complaint, specifically, is that the way this book presents statistics and explanations is so sparse and inadequate as to be nearly useless. It has plentiful exercises--which I like (because, as other reviews have said, repetition is the only way to learn a new language or skill)--but it's woefully inadequate in how it goes about conveying the foreign concepts of statistics to a newcomer. It expects a student (me, in this case) to understand all the complexities of a concept after having read the paragraph or two of explanation given in the textbook. Consequently, all the wonderful examples are useless because the book doesn't grant the comprehension to do the exercises. I've already bought one more statistics textbook to supplement this one (which is, unfortunately, used in my stats class) and will probably be buying another one. I'm desperate for a decent stats textbook because this one isn't remotely decent. Get it for many examples to work (though I don't understand why a text book would provide exercises without providing a key for said exercises), but not for learning the concepts. This book provides the key to HALF the exercises...why?

This is mostly just a glossary for terms in each chapter. I was expecting more of a "cliff notes" type book. If you need to review vocabulary, fine, but if you want more, this is not the book for you.

This book was delivered when I expected it, is in good shape and is easy to understand and follow

This book was delivered when I expected it, is in good shape and is easy to understand and follow

This is one of the two worst textbooks I have ever read. One large liability is that, even though it is not calculus-based, it almost always omits algebraic derivations that would be helpful in understanding the underlying relationships of models and methods. Particularly frustrating was the chapter on inference for regression, which was impenetrable and sloppy in its organization. The order of presentation jumps around to distraction and concepts are used "coincidentally" in earlier chapters that aren't fully developed until later chapters.

The answer key for odd-numbered questions is perfunctory. The authors would have served the needs of students far better had they used fewer problems at the end of the chapter and provided more well-developed solutions. In fact, some very important and key concepts are tucked away in the problems and will escape your notice if you don't do them. There are additional chapters shown in the table of contents that aren't actually in the textbook -- you have to get them from the accompanying CD-ROM and print them out yourself! How cheap is that? To make matters worse, the odd-numbered questions at end of these chapters do NOT have the answers available anywhere that I can find. There is more supplemental material available as PDFs on the CD-ROM (for example, on transformations) that liberally refers to images that are not on the PDF!

Many examples show the outputs given by various software packages, but don't provide the outputs given by the most important freeware application, R. The authors are obviously bent on promoting the interests of the developers of proprietary software.

This was a wretched introduction to statistics. I feel like I need to take a whole new course. At our school, different sections of our statistics course use different textbooks. I would have avoided the sections using this textbook if I had known how badly written it is. For my purposes, pure junk. I am a quantitatively capable student with a firm background in mathematics through multivariate calculus, linear algebra, logic, and game theory. I found the presentation of statistics in this book bafling. PROFESSORS -- PLEASE DO NOT USE THIS TEXTBOOK IF YOU CARE WHETHER YOUR STUDENTS WILL LEARN HOW TO USE STATISTICS SUCCESSFULLY IN THEIR ACADEMIC FIELDS. I found, in the course that I took, it succeeded only in undermining students' strengths and leading them further from what they could have achieved with good textook as a foundation for learning. Must to avoid.

the book was in excellent condition, and it arrived on time.

I am a teacher of AP Statistics (since its inception in 1997) with a master's degree in statistics. I agree with many of the previous reviews that this is NOT a mathematical statistics textbook. If you need a textbook that goes through the mathematics, especially the theory, behind the statistics then you need to look at another textbook. This book was designed for a statistics course for non-mathematicians (especially students with no Calculus background), which is why it is not terse and it has lots of repetitive problems. I have used four different textbooks for AP Statistics as a teacher and this is the only one I have ever used that got consistent rave reviews from my students. For a high school student with only Algebra II or pre-Calculus as a background, this is a very readable and easy to follow textbook, as opposed to other AP Stat textbooks which incorporate too much of the theory for this level of student. Many of my students have told me that this was the only math textbook that they have ever been able to read, and understand, on their own.

I am a teacher of AP Statistics (since its inception in 1997) with a master's degree in statistics. I agree with many of the previous reviews that this is NOT a mathematical statistics textbook. If you need a textbook that goes through the mathematics, especially the theory, behind the statistics then you need to look at another textbook. This book was designed for a statistics course for non-mathematicians (especially students with no Calculus background), which is why it is not terse and it has lots of repetitive problems. I have used four different textbooks for AP Statistics as a teacher and this is the only one I have ever used that got consistent rave reviews from my students. For a high school student with only Algebra II or pre-Calculus as a background, this is a very readable and easy to follow textbook, as opposed to other AP Stat textbooks which incorporate too much of the theory for this level of student. Many of my students have told me that this was the only math textbook that they have ever been able to read, and understand, on their own.

I am very happy with this book, I needed it for a class. The price was much cheaper than the bookstore at school and it was in excellent condition

I agree with the previous reviewer Jason --- this book is very good in helping a first-time learner understand statistics applied to real-life situations. Usually university teachers don't make good choices for course textbooks but in this case, this book is absolutely the most correct choice.

This introductory statistics book is unlike any other I read, so it is understandable why it received negative reviews. First off, it deals with "the practice" of statistics, so don't expect mathematical explanations of the statistical analyses presented. Second, it thoroughly explains the conceptual basis and applied aspects of statistics, so don't be surprised if it is a bit more wordy or repetitive than other statistics books. Reenforcement is necessary when learning a new language, and it doesn't assume mathematical formulas are understandable without explanations.

Its highlight is its coverage of collecting data. Most statistics books don't even mention how data is collected, or should be collected; they only show you how to analyze it. General principles of sampling and experimentation are licidly covered, as are the implications of using these two fundamentally different approaches to research.

The second strong point of this book is its general overview of statistics. It shows how different analyses are used for different types of data (categorical vs. quantitative), although the general premise is the same--relationship between variables.

Finally, it makes a connection between real data and theoretical distributions. Most statistics books start off saying, "assume the data follow a normal distribution" but real data never does. Moore and McCabe explains how we can use a mathematical formula to model our real data, and the advantages and limitations of doing so. This is the bridge necessary to place the theoretical world of probability and mathematical statistics into the real world of research and data analysis.

This is still my favorite introductory statistics book, it is unique and inciteful, while others are clones and impractical. It is for researchers, not statisticians. If you are a researcher and have reviewed many introductory statistics books you will see the value of this one in explaining how statistics work, instead of just showing formulas.

This text is horrible for learning statistics. Please, instructors, choose something different. This text takes concepts that could be quite simple and explains them so badly they become unrecognizeable. As a student, you have to really dig through the text to extract the basic ideas. If you read each chapter a few times, you eventually realize what they're saying, and it's not difficult at all - they've just made it that way. Exactly what you don't want in an introductory text. There are many good problems to work through, but that is the only real strength of the book. I was interested in statistics before I met this book.

If you are trying to learn Statistics on your own, make another choice. I took a Statistics course via distance learning, and this book did a lousy job explaining the material.

I used this book as a text for a very large (~150 students) first-semester course in biostatistics for public health grad students. It is lavishly illustrated and has huge problem sets at the end of each chapter. It uses lots of examples. I did not think it did a good job of either conveying either intution or theory. Many definitions are far short of rigorous, and students seemed dissatisfied with the resulting uncertainty. The problems are far too repititive. I would prefer a more terse book which provides definitions and theorems that actually can be used.