It's difficult to treat this statistical hallmark fairly from a modern perspective, and particularly from the perspective of a social scientist. FisheIt's difficult to treat this statistical hallmark fairly from a modern perspective, and particularly from the perspective of a social scientist. Fisher's work, while undeniably fundamental to current statistical techniques in psychology, lay firmly in the applied realms of genetics and agriculture. Whereas it is possible to read his treatment of Latin Squares in plots of land, for example, as generalizable to the design of factorial behavioural experiments, holy hell is it tedious: it demands not only a translation of antiquated scientific prose to modern equivalents but also a translation of agricultural terminology to that of participant groups. His approach is, moreover, deeply mathematical. I skipped over much of the final chapter which, near as I could tell, dealt with algebraic proofs of the sufficiency and biases of parameter estimates-- topics that I had a hard time following in more accessible terms in my courses, lacking as I do a strong mathematical background. Having both taken and taught the topics covered in the book, I know that most students in my field have a difficult time with this dry approach to statistics, preferring instead to have the concepts bootstrapped to their research programs. Nowadays, very few experimentalists are statisticians, and vice versa.

Nevertheless, the book certainly has value as an alternative perspective for those already familiar with the techniques. Fisher illustrates the fundamentals of the exact z test, single sample and paired t tests, confidence intervals, factorial tests, chi-square tests, and others. The way he explores them is vastly different from how I learned them, but the comparison was a worthwhile exercise. My existing knowledge allowed me to better understand his approaches to the same concepts, and to expand my understanding of their theoretical foundations. Old tricks, new angles. And a few fun jabs at Neyman and Peason, to boot!

I would not recommend this book to anyone without a strong grasp of the Fisherian tradition of data analysis. Those few of you well-equipped, on the other hand, may find it worth your while....more

Full disclosure: I cannot speak to Chapters 15 or 16, as they were not part of my course. It was an advanced graduate level course on analysis of variFull disclosure: I cannot speak to Chapters 15 or 16, as they were not part of my course. It was an advanced graduate level course on analysis of variance.

This is probably the clearest and most thorough statistics textbook I've ever come across. It tackles analysis of variance from the ground up, presenting it in terms of the statistical model comparisons that underlie stats packages like SPSS or SAS (and the theory that built them) and in this way demonstrating the ultimate cohesion of all analyses, for any design, based on the general linear model. Maxwell and Delaney write with impressive patience and clarity on increasingly challenging topics-- each one is broken down in turn and shown to be a logical and mathematical extension of the basic concepts. Examples are used throughout to illustrate concepts, and exercises are given at the end of every chapter. Moreover, syntax for stats packages is occasionally provided.

Though the course was heck of tough, it was also incredibly rewarding, and this textbook perfectly complemented the lectures and assignments to ease my understanding. I had only two small complaints about the text. First, that it grows a bit repetitive in extensions from lower- to higher-order designs of the same type; while I understand they were trying to be as explicit as possible, it felt redundant at times. Second, especially further on, that some sections involved drastic leaps in complexity certain to flummox readers with a lesser grasp on the materials. I came to this book with several upper-level statistics courses under my belt, but the book is meant to be used with undergraduates as well. Even so, the optional endnotes regularly flummoxed me, and I found myself wishing they were written with just a touch more consideration for readers without mathematical backgrounds-- I was terribly interested by the ideas, but often could not follow the maths.

Aside from those two details, however, I found an unexpected enjoyment in learning from this book, and would recommend it as required reading (or at least required owning, for reference) for any graduate student in psychology....more

An interesting, if repetitive, book about the practical applications of statistics, ranging from business to government policy, from law to internet sAn interesting, if repetitive, book about the practical applications of statistics, ranging from business to government policy, from law to internet searches. It contains a large number of fascinating (and occasionally horrifying) pieces of information, enough to satisfy a basic curiosity about how statistics are revolutionizing decision-making in fields as diverse as Hollywood blockbuster filmmaking and poverty reduction, but not enough to really appeal deeply to those who already use statistics in their daily lives.

The style and tone of the book are less than impressive: at times leaping schizophrenically to loosely connected topics, at times pulling quotations from hardly-referenced sources out of the blue and shoving them awkwardly in (perhaps as nods to those economists the author seems eager to associate himself with), and often repeating a few of the book's simpler ideas over and over, beating them against your head in a frustrating, condescending manner. It would have benefited from more careful editing for precision and conciseness.

I certainly give Ian Ayres kudos for writing a pro-statistics book for the "uninitiated," but I regret that he doesn't actually explain much of the basic math that would allow those uninitiated to take their learnings beyond the "interesting factoid" zone and into a realm that would encourage them to apply this new knowledge themselves. Surprisingly, he glosses over such simple mathematical concepts as regression, referring to the idea behind the technique but never really examining it, and yet tries to explain the mathematical logic of Bayes theorem in a few short paragraphs (far too vaguely to be of much use to those who have never heard of it before, mind you). This strikes me as inconsistent and off-putting. Though nowhere NEAR as off-putting as the use of colloquialisms like "egghead," "gearhead" and "Super Crunchers" (ALWAYS with capital letters, to signify his term's importance and sear the title of the book directly into your brain), offered incessantly in some strange, awkward, self-depreciating attempt to make stats "cool" to the predominantly lay-person audience he hopes to convert.

I cringed. Often.

So to clear up any misconceptions.... Dear lay people: we don't call ourselves "Super Crunchers". We don't call it "Super Crunching," (or just "Crunching" for short, for that extra cool factor). We call it stats. Or data analysis. Or maybe my university just isn't as rad as Ian Ayres' is.

/end tangent

If you're looking for a quick, factually interesting read, and have no compunction about removing the meat of statistics in favor of the admittedly more absorbing, glossed-over fat, then I definitely recommend this book. I'm not sure it was 100% for me, but I still picked up some neat facts from it....more

This book is an accessible, truly fascinating trip through the lives and accomplishments of the men and women driving the relatively new field of statThis book is an accessible, truly fascinating trip through the lives and accomplishments of the men and women driving the relatively new field of statistics forward, with force, in the twentieth century. It's surprisingly gripping, given its subject matter, written to appeal to people with limited prior knowledge on the topic, but who have an interest in the history of science. I would definitely recommend it to students in any field of science, as a light, entertaining, bit of exploration to accompany their formal education.

As an aspiring researcher who uses statistics on a regular basis, I found the approach taken in this book to be both inspiring-- to discover that so many geniuses came from humble beginnings-- and alarming-- to see in such plain english the gaping holes in modern statistical techniques....more

David Howell writes in a fairly easy-to-follow manner and is decent at explaining statistical concepts-- not great, however: he slips into over-compliDavid Howell writes in a fairly easy-to-follow manner and is decent at explaining statistical concepts-- not great, however: he slips into over-complication of a few ideas while flippantly running over others that could use more thorough consideration. As a result, the book alternately feels as though it were written for undergraduates and/or statisticians beyond my own abilities. I don't feel that I personally got much out of it.

His methods, theoretical opinions, and variable designations differed widely from those used by my professor, so the textbook often served to hinder rather than aid my understanding of the concepts. I think a different text (or no text at all?) would have better served my graduate level statistics class, but I am grateful that it was at least easy to get through (i.e. didn't make me cry and pull my hair out)....more