Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking

Statistics is hard, and I'm speaking as someone with a modest mathematics background. My approach has always been to build up a proper intuition on a particular subject of interest. This book is exactly what I've been looking for for statistics. And what's more, it's immensely readable and fun! The book revolves around the fundamental concept of confidence interval (CI) in how it aids statisticians in making conclusions. An in-depth, intuition-building treatment of CI is presented satisfyingly in the first few chapters. And then, statistical significance is discussed; Dr. Motulsky carefully describes how it relates to CI, and imparts wisdom on how one should properly interpret statistical significance, taking into account the False Positive Report Probability (FPRP) and prior probability. Statistical power and how it's affected by sample size, effect size, and significance level are discussed. Various statistical tests, techniques for fitting models to data (linear and non-linear regressions, logistic regression, etc.), and the rest of statistics (e.g. ANOVA, nonparametric methods, etc.) are presented, and again, the theme of CI (and statistical significance) are weaved throughout. This book is incredibly satisfying not only because of what I mentioned above, but also for how it guides one through understanding the common traps and pitfalls and how to avoid them. For example, Dr. Motulsky shows how easy it is to be fooled by multiple comparisons as one tries to make crisp decisions based on statistical significance from limited data. Another example is how common it is for well-meaning researchers to do, among other things, P-hacking in their quests for statistical significance (and thus many published results are not reproducible). In the end, this book definitely accomplishes its goal: It builds one's intuition on how to think about your data, how to apply statistics properly, how to avoid pitfalls, and how to publish statistical results properly. If you're like me, you want to learn from Dr. Motulsky. I highly recommend this book.

Just an excellent text. Probability and statistics are about removing noise. Being able to perform calculations is only One aspect, and the easy portion of data analysis typically. This text provides the wisdom needed to use probability and statistics for their real purpose, removing noise in data and in data interpretation. Absolutely excellent book for undergraduates or graduate students in probability/statistics or any science (hard or soft). I can’t emphasize enough how useful this text is in helping beginners and intermediate users of probability and statistics avoid making errors in data interpretation, and really helping them understand what their data means and the context in which it has said meaning.

I can’t imagine it is easy to write a text about statistics that is funny, interesting and useful. The author obviously put a lot of effort into this! Very enjoyable and refreshing read for my course.

I have purchased the paperback, 4th edition, 2018 Oxford University Press version of this book. The author has a clear and informative style, and covers statistics and correct interpretation of statistics from the perspective of an informed reader in the biological sciences who is familiar both with academic writing and with statistical tests in general. It's dense and not going to be a quick read, but it is very important stuff for researchers in medicine, anthropology, probably any of the social or biological sciences and should be covered in far more introductory classes. That being said, my edition is less than intuitive from chapters 34 to 37. Chapters 35, 36, and 37 are intriguingly but annoyingly mixed into each other, with 33 ending into the last three pages of chapter 36, which is followed by the first three pages of 37, truncated with the sudden appearance of chapter 35. Chapter 34 is regrettably absent in entirety, which is too bad for many reasons. I hope no one else experiences this issue, and it is only a rare deviation from the normal distribution of complete books. If there are others who are missing chapter 34, however, please push forward the information to the publisher! It's a great book that should be received whole. Highly recommend the book, highly recommend care when purchasing and review upon receipt to ensure the book is complete and includes all chapters.

It's the second time I bought this (first was long time ago, lent, never returned, but it was ok, cause totally "used"). This is a really new edition, with some new examples (Trump election is one of those!), same great explanations. Make biostatistics something pleasant to read.

Thoroughly enjoyed learning from this book. It is an ideal non mathematical text for beginners

I am by no means a germophobe but this book grossed me out. The book itself is great, but without a teacher to narrow down to what is important it is still filled with unnecessary details. It was written by an MD who seems to have lost track that over 99.99% of the population will find the book challenging and be turned off by it. The examples could be simpler and more concise. I would start with the chapter summaries, then the assumptions, and only dive into the text for projects, exams etc. I did that and am getting an A!

So many biostatistics books are poorly written, this one is not. This is the book I reach for when looking for an explanation of a statistical concept.

Great statistical text book - very easy to read and comprehensive

He leído muchos libros de estadística y sin duda este es el mejor. No digo que tenga TODO pero quién lo lea aprenderá estadística para una mejor investigación. Es mi libro de cabecera para dar clases y de recomendación.

The most comprehensive statistics book ever had

Wonderful book. Makes very clear all the traps and manipulations hidden in the statistical jargon. The best section to me is "what a statistically significant result means": there are 7 possibilities, and only one is the one you expect (ie there is a relevant effect). All medical doctors should read this.

Very helpful