Biostats Book Club
1 AI - Garbage in, garbage out (but still fun)
I was playing around with AI image creation this week and asked Microsoft Bing to create an image with ‘biostatistics book club’ as a prompt - this is what it came up with:
Hmmm - who would have ever thought talking about biostatistics could be so interesting.
Then, for even more fun, I asked Bing to create another image using ‘biostatistics fight club’ as a prompt and it gave me this:
Yep, just what I imagined a bunch of pugilistic stats-nerds to look like.
2 Some Biostats Books Recommendations
Anyways, I have significantly digressed! The point of the post this week was to draw your attention to some biostatistics textbooks that I have referred to a lot over the years and that I keep in hardcopy on a nearby shelf and also as digital editions. The physical versions are well-worn, especially the first that I’m going to recommend. All four of these are pitched with more ‘applied’ rather than ‘theoretical’ biostatistics in mind (my eyes start to glaze over when I see too many formulae on a page) and at a predominantly introductory level - although some certainly delve into more advanced concepts as well. If you are interested in having a good basic biostatistics reference (or two), than maybe take a peek at some of these.
2.1 Essential Medical Statistics
I can’t recommend this book enough. It’s now over 20 years old but that doesn’t mean it’s dated - the ‘essentials’ of statistics, well, haven’t really changed. Kirkwood’s book explains statistical concepts in such a clear and concise manner that it makes (for me at least), understanding them much, much easier. It strikes a good balance in covering all the important ideas in enough depth while still maintaining a relative lay language style.
2.2 Intuitive Biostatistics
The author of Intuitive Biostatistics is also the brains behind the Prism statistical software. You’ll be pleased to know there are almost no formulae written amongst its pages and I think a reasonable summary of the authors intentions is to provide a ‘common-sense’ treatment of statistical ideas. The book is littered with teaching examples as well as sections on ‘Q & A’s’ and ‘Common Mistakes’ and their potential solutions. Get the latest edition.
2.3 R for Data Science
if you are one of the ‘cool kids’ and use the tidyverse approach to coding in R, then this is probably worthwhile having. There is a free online version as well. R for Data Science is predominantly aimed at data-wrangling and preparing your data for analysis - tidyverse style. I don’t consider myself a great statistical programmer, so I have found some elements of this a little difficult, but the more basic stuff is really useful (and coding should be a daily journey of self-improvement anyway).
2.4 The R book
The R Book differs from R for Data Science in that, yes it’s a book about coding in R, but the focus isn’t just on data-wrangling. This book will give you almost any bit of code to run nearly any statistical procedure in R that you could imagine. In that sense it’s also a worthwhile reference. Mind you, as a result of the breadth of material it covers, this is a BIG book!
I hope you find these helpful in your statistical learning.