Customer Reviews

Introduction to the Practice of Statistics, 4th Edition (Book & CD-ROM)

5 star:		(9)
4 star:		(4)
3 star:		(2)
2 star:		(0)
1 star:		(2)

 

 

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.

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 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 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.

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 course 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.

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.

Moore and McCabe is a great textbook for an introductory 1 semester course in statistics. The text is clear and not cluttered with unnecessary details. The examples are accompanied by many colorful charts and graphs/plots. They take a more "classical" approach to teaching statistics (which appears to be falling out of favor) but the book is very good.

This book follows the standard format of descriptives to probability to inference, with many examples and exercises. It is perfectly suitable for a course containing a mix of students who may or may not continue their studies in statistics, but is otherwise unremarkable.

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.

Moore and McCabe's Introduction to the Practice of Statistics is very helpful. It gives a great overview of the topics and explains the principles well. The exercises solidify the concepts taught in each section.