Discovering Statistics Using R

Honestly such a helpful book. Of course we are in the era of AI but it doesn’t explain everything well. This book explains the stats, the code, and why you’re doing xyz. It’s a great book and puts it all in simple terms. Here I am in my post doc and I still refer to it!

This book fills a niche that very much needed to be filled. It is both a review of basic statistical concepts and directions as to how to perform the corresponding analyses/tests in R. It's light on theory of course, but supplying proofs and in-depth descriptions isn't what this book is about. Although I'm a bit rusty, I've had a great deal of graduate level statistics, none of which emphasized application. This book is an excellent guide as to how to actually apply statistics. Extremely welcome is its emphasis on underlying assumptions. In my theoretical statistics classes, the Central Limit Theorem was the answer to almost all questions involving assumptions. As the authors point out, even with a sample size that's sufficiently large, the CLT does not always guarantee normality. I also like that the authors give complete steps in each chapter. Thus the entire coding to accomplish something is present and you don't have to go looking for how to accomplish some preliminary step before you can do the current procedure. At the end of each chapter is a list of what R packages and functions have been used. The authors do include some sophomoric humor, maybe to make this more palatable to undergraduates, but this doesn't become annoying. Finally the authors appear to like cats, a mark in their favor. One word of warning, Field may not provide a context for something—a test, a transformation, etc. Readers are advised to look at the references he provides at the ends of the chapters. For instance, his later presentations on bootstrapping will make a lot more sense if you’ve read the paper by Wright, London, & Field he suggests. This can be found online. When presenting the Fisher transformation of Pearson’s r to a z-score in Sect. 6.3.3, he doesn’t tell you that it should be used only in tests of null hypotheses rho = some constant not = to 0 or to 1; where .3 < |rho| < 1, r’s sampling distribution will tend to be skewed, making the Fisher transformation necessary. Not knowing this context, given in Chen and Popovich, one of the references at the end of Chapter 6, could cause a reader to use the Fisher transformation inappropriately.

The writing style is highly accessible, fun, varied, and rich in detail. Simply a superb way to get going quickly in R AND in statistics, but even if you have considerable stat under you belt, as I do, it provides an excellent review of concepts, and their implementation in R. I am pleased in every way with this massive survey of the field. With this in hand I know I can go off in whatever direction of specialization I require. There is simply no question in my mind that this the best starter book for both stat and R (and learning the two together, these days, just makes sense). It turns out to be far better than I expected. Loaded with extra information, plenty of fine-grained detail, well worked-out examples, and unexpected humor, this makes its subject just about as accessible as can be done. A great value!

I must start by saying that this is an excellent book and one of the most approachable that I've found for teaching students new to R. This could be a five-star book if the author would consider a few things, many of which would shorten the length of the book considerably or as an even better alternative the author could place even more code and data examples in the book with the saved space: 1) Ditch the discussion and elements of using R commander. This is a hold-over GUI need from the author's last book on SPSS. Learning R well means getting into the code and the command line and staying there. GUI based add -on's like R commander just get in the way and you cannot even run all of the available stats in it anyway. Users are buying this book to learn the code, so teach them the code only! 2) Dispense with the witty banter. The author clearly has a sense of humor and likes it, but there are needles pages in the book, essentially the start of each chapter, that are just silly asides that serve no purpose and take up a LOT of space. The humorous examples for many of the datasets are great and a welcome change to a stuffy stats book but the excessive chatter and jokes end up taking up more space that could be used for more practical examples or hints for the new R user. I also found these witty asides distracting once I was knee-deep in the methods of running a test. 3) Dispense with the repetitive instructions and inefficient methods. Do we really need to see the code for loading a new data set over and over? Does that data set really need to be a .dat file? It would be better if the author would stick with conventional excel files (.csv) that most people are commonly going to use. Provide an early chapter for getting data into R and move on already! If you are coming at this topic from the life sciences (biological, ecological etc.) the author tends to favor planned contrasts a lot more than post hoc multiple comparisons which are much more common in these sciences. I would strongly recommend that you buy this book, but find a good bio stats book to read in tandem so you can get your head around more biological examples.

Andy Field writes some of the most intuitive and entertaining accounts of statistics available, and this book is no exception to that standard. This book is geared towards those who want to start from the beginning and progress through a complete account of the most common methods in statistics based on the general linear model. If you are a beginner, this is one of the best places to start. If you are experienced, this book is a great reference to have around. The most enjoyable aspect of this book, aside from its humor, is that Field addresses issues of using robust statical methods when assumptions are not met in the data. Instead of glossing over the issues, Field provides the most recent findings in the field and even examples of how to run robust tests in R. However, note if you want to do something very complicated with robust methods, this book is not a cure all, and you would be hard pressed to find one that is. With regards to R, this book will get you up and running with R even if you have no previous experience with R or programming languages in general. However, a few of the R libraries have changed since this edition's publication, so you will need to search a bit to fix a few errors, but it's not hard and is good practice. Finally, I must mention that Andy Field has gone out of his way to provide datasets and examples like no other author I have encountered. The book has a companion website full of these datasets and all of the R scripts used in the book. Additionally, the companion website is packed full of extra material for each chapter in the book. Finally, Field has several videos posted to the website which includes a lecture series on statistics.

Robert Abelson wrote in his "Statistics as Principled Argument" that, in his experience, "most students do not _really_ understand statistical material until they had been through it three times -- once for exposure, once for practice, and once for the drawn of genuine insight." Well, I was one of these "most students", but now that I am more aware of the challenges of learning and teaching statistics (see ASA GAISE), I see in this text the opportunity to shorten this path for other students. The authors did a terrific job in making things clear, practical and funny, real LOL funny!

Andy Field simply has a way with making a scary subject- statistics (dare I say it) fun. This book is an excellent step-by-step guide that a beginner will feel comfortable with, but also serves as a great reference for someone like me, who has a background in stats. More importantly, it shows how to perform various statistical analyses using R - which is a great & highly in demand skill. When you buy this book, you have access to example data sets and supplemental material - it's as if Andy is there to help you along the way. If you are freaked out about learning stats, or feel like you don't understand the theory behind how the tests work and when you should perform certain ones, then this book is you. Admittedly, I'm a nerd, so the fact that I actually do enjoy reading the chapters and working the examples might be biased. It's possible that the general population might disagree with me. I do, however, think that Andy's sense of humor and clear examples, rather than my own nerdiness, explains more unique variance in predicting why I so highly recommend this book. Even if you're dreading learning stats, I think this book will show you how much fun it can be- or at least that you don't have to be a math wiz to get the basic concepts. Five stars all the way!

Product was described as new but has a few creases and stains. Content is what it says. Discovering statistics with R. Absolutely riveting! Next time, I'll stick to ordering straight from the publisher instead of saving a couple bucks.