How to Get P-Values Less Than 0.05 in Biological Research: Practical examples of how to increase power, reduce sample size, and choose better statistics tests Paperback – February 16, 2024

One of my pet peeves with (bio)statistics is that the field is jumbled with numerous methods. The cynical among us data scientists might grumble that this is so because every stats grad student needs a novel one to graduate. Normally the advancement of the field isn't an issue, except that so many statistics books think their job is to catalog these methods in an academic fashion, with pragmatic concerns never making an appearance.This is why I love this textbook so much. It takes a completely different approach where the pragmatic concerns come first, and are addressed head-on. As new and realistic scenarios that a researcher might encounter with their data are discussed, the author then explains why a different method is justified. By putting the realistic problem first, it motivates the reader to learn because they now care about how to solve that problem. The author does an excellent job in describing the methods, giving intuition about what it is doing, without getting "in the weeds" about how the algorithm works. He also works through real datasets (available online) using the R programming language, and plots the data and resulting statistics, so that the reader can quickly see how the combination of data and statistical programming can help answer real-world problems. He also provides links and references to related content, for those who want to learn more on a given topic.I think that this book should be required reading for every incoming biology and medical student. It will help them immensely in their coursework and research.

What a breath of fresh air: a terrific statistics book that doesn't cost a hundred dollars. This book provides practical information on sample size, power calculations, various types of analyses, recommendations for free PDF versions of various texts. Being a 2024 release, you get up to date suggestions. And if you are trying to learn R, there are numerous examples. This is really the best 215 pages on my bookshelf. (Review submitted by former chief statistical consultant at The Ohio State University.)