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The Analysis of Biological Data
The Analysis of Biological Data provides students with a practical foundation of statistics for biology students. Every chapter has several biological or medical examples of key concepts, and each example is prefaced by a substantial description of the biological setting. The emphasis on real and interesting examples carries into the problem sets where students have dozens of practice problems based on real data.The third edition features over 200 new examples and problems. These include new calculation practice problems, which guide the student step by step through the methods, and a greater number of examples and topics come from medical and human health research. Every chapter has been carefully edited for even greater clarity and ease of use. All the data sets, R scripts for all worked examples in the book, as well as many other teaching resources, are available to qualified instructors (see below).
2639 pages, Kindle Edition
First published July 1, 2008
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178 people want to read
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Displaying 1 - 16 of 16 reviews
June 16, 2025
Okay, first things first. I really do not like stats, there's just something about them that my brain just does not like. But, Whitlock somehow managed to make them make sense (mostly) and while there are still areas I get myself in a bit of a mess with, this book helped me get out of such messes about 9 times out of 10. Granted I've only been doing what many would probably see as relatively simple stuff but for me this is a bit of a miracle. And not only does Whitlock bring biology to stats allowing for a much more interesting read (his first example test injuries sustained by cats falling from windows, seriously), there are also some delightful interlude essays that are great to read and really help break-up the stats pain.
April 30, 2018
I had to use this book for a biostats class and I literally read it in its entirety. One of the clearest, most straightforward, easy to understand textbooks in my college career. You can self-teach basic biostats with this textbook no problem.
October 12, 2022
not into it.
April 8, 2024
couldn't have wished for a better introduction to statistics, saved me in my second year of uni! i think i got recommended this book by a tutor and it enabled me to actually enjoy stats instead of leaving every class frustrated <3 genuinely one of my favourite books of all time, i don't even care.
December 12, 2020
It's a really great way to introduce yourself to R. While the book is slow and boring the corresponding R series is really great.
December 8, 2020
This was the textbook for my graduate level statistics course this semester, and we read every chapter except the last one on meta-analyses combining data from multiple studies, so I think I am in a good enough position to give a just review of this book.
"The Analysis of Biological Data" (Second Edition) by Whitlock & Schluter is a well balanced textbook on statistics which focuses on biological applications. This is a great accompaniment for any undergraduate or introductory graduate level course in this field. Every scientist should learn statistics, and this book is a great place to start.
I give this 4 out of 5 stars in part because this book alone will not teach you how to do everything in statistics that it mentions; this is no substitute for taking a quality statistics course. There are certain computations that need to be done in computer programs like Excel or R, so you might also need professors, teacher assistants, or online modules to help walk you through preparing specific data and conducting calculations in order to get truthful results.
While this won't be able to teach everyone statistics in one go, there are invaluable statistical tables, additional references, and directions to online learning to stimulate further learning. Every chapter has both practice problems and assignment problems, for which the practice problem answers are at the back of the book, so while professors might list assignment problems for homework, students can use the ~15 practice problems for every concept to study in preparation for exams etc. Every chapter also includes a summary, and computational heavy ones have quick formula summaries at the end prior to the practice problems to provide a quick reference for what was covered in that chapter.
The interleafs are bonus 2-3 pages of discussion on topics pertinent to statistical research applications, and I am quite glad I read all of them. Interleaf topics included: pseudoreplication, statistical significance vs. biological importance, correlation does not require causation, controls in medical studies, publication bias, using species as data points, and even a flow chart with advice on which statistical test should be used for which kind of data (categorical vs. numerical data, data pairing, etc.). Whitlock & Schluter take pains to make every example adapted from real published datasets and taking examples from a variety of biological studies (not just medical, not just evolution, etc.), which is one of my favorite aspects of this textbook.
And so, while someone outside of school will not be able to buy this textbook and teach themselves statistics, this is an invaluable resource for anyone practicing the scientific method or referencing statistics in their professional life. This is the best resource on statistics I have been able to find, and I am so glad it was the key reference for our course this semester. I am thrilled that I bought it and will continue to reference it for years to come.
"The Analysis of Biological Data" (Second Edition) by Whitlock & Schluter is a well balanced textbook on statistics which focuses on biological applications. This is a great accompaniment for any undergraduate or introductory graduate level course in this field. Every scientist should learn statistics, and this book is a great place to start.
I give this 4 out of 5 stars in part because this book alone will not teach you how to do everything in statistics that it mentions; this is no substitute for taking a quality statistics course. There are certain computations that need to be done in computer programs like Excel or R, so you might also need professors, teacher assistants, or online modules to help walk you through preparing specific data and conducting calculations in order to get truthful results.
While this won't be able to teach everyone statistics in one go, there are invaluable statistical tables, additional references, and directions to online learning to stimulate further learning. Every chapter has both practice problems and assignment problems, for which the practice problem answers are at the back of the book, so while professors might list assignment problems for homework, students can use the ~15 practice problems for every concept to study in preparation for exams etc. Every chapter also includes a summary, and computational heavy ones have quick formula summaries at the end prior to the practice problems to provide a quick reference for what was covered in that chapter.
The interleafs are bonus 2-3 pages of discussion on topics pertinent to statistical research applications, and I am quite glad I read all of them. Interleaf topics included: pseudoreplication, statistical significance vs. biological importance, correlation does not require causation, controls in medical studies, publication bias, using species as data points, and even a flow chart with advice on which statistical test should be used for which kind of data (categorical vs. numerical data, data pairing, etc.). Whitlock & Schluter take pains to make every example adapted from real published datasets and taking examples from a variety of biological studies (not just medical, not just evolution, etc.), which is one of my favorite aspects of this textbook.
And so, while someone outside of school will not be able to buy this textbook and teach themselves statistics, this is an invaluable resource for anyone practicing the scientific method or referencing statistics in their professional life. This is the best resource on statistics I have been able to find, and I am so glad it was the key reference for our course this semester. I am thrilled that I bought it and will continue to reference it for years to come.
February 2, 2009
Here is a livejournal entry I made upon first cracking open this wonderful statistics textbook:
My biostatistics textbook is probably the nicest textbook I've ever owned. The textbook for my introductory statistics course last semester not only pales in comparison, it looks downright awful (in reality, it wasn't half bad and I sort of liked it at the time. This text is just so lovely! It's a good inch and a half shorter both lengthwise and widthwise, as well as being surprisingly light for its depth. The layout is really clean, done in nice colours and with small photos and figures where appropriate. Unlike most modern textbooks (which appear to be designed for ADD sufferers due to the amount of unnecessary crap surrounding small blocks of text), this book has nice wide margins, clean pages of text and minimal distractions.
Content-wise this book also competes for most useful reference text I've ever owned. The introductory book last semester was next to useless in terms of applying its content to a real biological research project. To demonstrate the applicability of this text, I need only to mention one chapter, entitled "Meta-analysis: Combining Information from Multiple Studies". What other introductory level text includes a chapter on how to do a bloody meta-analysis? None as far as I know! That's some super relevant and interesting stuff. Not only that, but the interleafs between chapters talk about a number of important, slightly off-topic stuff that can't be put into any particular chapter but is useful to know a bit about. Stuff like publication bias and using species as data points. Brilliant, absolutely brilliant. I plan on keeping this as a reference for the remainder of my scientific career.
My biostatistics textbook is probably the nicest textbook I've ever owned. The textbook for my introductory statistics course last semester not only pales in comparison, it looks downright awful (in reality, it wasn't half bad and I sort of liked it at the time. This text is just so lovely! It's a good inch and a half shorter both lengthwise and widthwise, as well as being surprisingly light for its depth. The layout is really clean, done in nice colours and with small photos and figures where appropriate. Unlike most modern textbooks (which appear to be designed for ADD sufferers due to the amount of unnecessary crap surrounding small blocks of text), this book has nice wide margins, clean pages of text and minimal distractions.
Content-wise this book also competes for most useful reference text I've ever owned. The introductory book last semester was next to useless in terms of applying its content to a real biological research project. To demonstrate the applicability of this text, I need only to mention one chapter, entitled "Meta-analysis: Combining Information from Multiple Studies". What other introductory level text includes a chapter on how to do a bloody meta-analysis? None as far as I know! That's some super relevant and interesting stuff. Not only that, but the interleafs between chapters talk about a number of important, slightly off-topic stuff that can't be put into any particular chapter but is useful to know a bit about. Stuff like publication bias and using species as data points. Brilliant, absolutely brilliant. I plan on keeping this as a reference for the remainder of my scientific career.
January 7, 2020
Wow.
It's not very often I come across a textbook that isn't dry and makes me wanna sleep two paragraphs in.
Sure, it helps that I was lucky enough to have one of the authors of this book by my professor (and a great one at that!), but really! This textbook is amazing!
Especially with statistics, I think it really helps when examples are worked out and flush with ALL the calculations you need to make instead of assuming the student has any decent mathematical skills!
Also, if you get a chance, totally look out for the little footnotes, they can be hilarious!
Just another small side note: This book is surprisingly small (read: stout) and surprisingly light for such a thick looking book!
It's not very often I come across a textbook that isn't dry and makes me wanna sleep two paragraphs in.
Sure, it helps that I was lucky enough to have one of the authors of this book by my professor (and a great one at that!), but really! This textbook is amazing!
Especially with statistics, I think it really helps when examples are worked out and flush with ALL the calculations you need to make instead of assuming the student has any decent mathematical skills!
Also, if you get a chance, totally look out for the little footnotes, they can be hilarious!
Just another small side note: This book is surprisingly small (read: stout) and surprisingly light for such a thick looking book!
February 23, 2015
This text is covers topics to satisfy those seeking to refresh their knowledge of statistics as well as those looking for an advanced understanding of theory and application. It's organized in a student-friendly well and the writing is superb - easy to follow and presented with a logical flow. So far, this is my favorite biostats book and it's the go-to reference book when working on data analysis.
May 16, 2015
The authors did a great job of making a somewhat dense not necessarily fun topic rather fun and interesting. While some of the math and the really hard points may have been hand waved over, for most students that will perfectly okay, as it teaches the important points well, and that's the main thing.
February 14, 2010
The greatest statistics book ever. Read this cover to cover two times before my exam - very informative and easy to follow.
Read
December 9, 2013YES I READ THE WHOLE THING AND I KIND OF LIKED IT
Read
April 20, 2017570.15195 W6137 2015
Want to read
March 4, 2016kmkmkmk
May 12, 2018
A well-written and thorough introduction to the use of statistics in the context of biology/ecology!
December 21, 2019
Fantastic statistics book giving a solid foundational understanding of both the theoretical and mathematical concepts.
Displaying 1 - 16 of 16 reviews