Intuitive biostatistics
"Thoroughly revised and updated, the second edition of Intuitive Biostatistics retains and refines the core perspectives of the previous edition: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes. Intuitive Biostatistics, Completely Revised Second Edition, provides a clear introduction to statistics for undergraduate and graduate students and also serves as a statistics refresher for working scientists. New to this edition: Chapter 1 shows how our intuitions lead us to misinterpret data, thus explaining the need for statistical rigor. Chapter 11 explains the lognormal distribution, an essential topic omitted from many other statistics books. Chapter 21 contrasts testing for equivalence with testing for differences. Chapters 22, 23, and 40 explore the pervasive problem of multiple comparisons. Chapters 24 and 25 review testing for normality and outliers. Chapter 35 shows how statistical hypothesis testing can be understood as comparing the fits of alternative models. Chapters 37 and 38 provide a brief introduction to multiple, logistic, and proportional hazards regression. Chapter 46 reviews one example in great depth, reviewing numerous statistical concepts and identifying common mistakes. Chapter 47 includes 49 multi-part problems, with answers fully discussed in Chapter 48.
Stats for nerds
3recommend
9mentions
3people
2015–2024years
100% recommend
Mentions per year
What Hacker News says
- I thought "Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking" combined with "PDQ Statistics" did okay. Way better than the awful instructor textbook taught as an intro to higher-level stats with R and S-Plus, and a useful supplement to the basic 101-102 level applied statistics textbook.
But probably the most useful thing was a college course where we implemented all the 101 & 102 level statistics algorithms in Excel, so you could see precisely what it was doing.
- Few of these books are “great works” in their field, but all changed my understanding of their topic.
- How to Read a Book by Adler and Van Doren. I was in academia when I read this and it had a huge impact on how I read and thought about academic papers.
- Intuitive Biostatistics by Motulsky. First stats book that I enjoyed. Emphasises the practice of statistics, particularly the assumptions and mistakes people tend to make.
- World War II Map by Map, published by DK. Had never previously been interested in WW2 history, but something about this took my interest. While reading it, I finally appreciated the scale and complexity of WW2.
- Bobby Fischer Teaches Chess. Working through this one meant I actually started getting better at chess!
- Common Sense Guide to Data Structures and Algorithms by Wengrow. The book that helped me become interested in data structures and algorithms, rather than being something I “should” learn about.
- I GREATLY enjoyed Intuitive Biostatistics by Motulsky.
It assumes that you're not going to calculate any test statistic on your own, you're going to use Excel, R, or his own software. Therefore the book contains little to no formulas. Instead, it's plain English going over (biology's) most common tests and attributes, and explains what the test assumes about your data, what you can and cannot infer from the output, and what the pitfalls are. VERY useful if you like me come from a 'push X button no clue why' background.
- Some books I enjoyed and which could be beneficial:
- Borges' Aleph, and his essays (for example Other Inquisitions). He was fascinated about concepts programming is famous for now, he would have loved recursion
- Mann's The Wizard And The Prophet. He uses the life of two highly influential scientists (Borlaug and Vogt) as exemplars for two ways of viewing progress, one highly focused on technological progress, one being sceptical of technological progress
- Wachter-Boettcher's Technically Wrong, about implicit biases in modern technology and how that excludes or mistreats people. Some good lessons if you want to start a company and get as many customers as possible ;) Similar book with more academic stringency: Weapons Of Maths Destruction
- Roman's Writing That Works, non-fiction/memo/email advice from big advertising guys, lots of good advice on getting your point across
- Fromm's The Art Of Loving & The Sane Society, two non-fiction books from a sociologist/psychologist on how to work on your relationships (i.e., love as a movie concept doesn't exist, it's mostly very hard work and self-critique), and how society as a whole has very broken goals. Becker's The Denial Of Death (on how fear of death is a major drive in life) comes from a similar place
- Wilson's How To Teach Programming - delves deeply into the psychology of learning and how to build stable communities, it's available for free so why not
- Statistics books are always good to mend your thinking! My favorite layman's introduction is Motulsky's Intuitive Biostatistics (no formulas, plain English), for a non-practitioner Wheelan's Naked Statistics is probably better
- Seth Godin wrote lots of good business books, pick one (I liked Linchpin)
- If you haven't read them yet, get the 'classics' of software management: The Mythical Man-Month, and Facts and Fallacies of Software Engineering
- Cuckoo's Egg is a marvellously fun non-fiction book about a programmer tracking a hacker in the 80s, one of the first international computer crime cases, featuring tons of fun low-tech hacks
- Statistics books I liked:
Motulsky's Intuitive Biostatistics - this one goes over all the usual methods used in science from distributions to t-test to ANOVA to regressions etc., the basics, but doesn't introduce the maths (you use R for that) but the assumptions and pitfalls of all of those methods.
Statistics Done Wrong: The Woefully Complete Guide - this is all the stuff that's going wrong in applied statistics, a bit short but enlightening
Discovering Statistics Using R - a whopper of a book (~1000 pages?), it goes through everything while also being funny (the constant humor may not be for everyone). Graphs, correlations, regressions, all the MLMs and GLMs, linear models etc. pp., their assumptions, how to run them in R, how to interpret R's sometimes annoying output, etc. pp. Like Motulsky's, but wayyyyy more in-depth on the language's specifics.
Naked Statistics - an intro to stats for laypeople with a focus on politics/economics, good for interpreting and assessing daily news
- Intuitive Biostatistics by Motulsky
Goes over the commonly used methods in science but only explains the intuition behind the method and what you can and cannot expect from the results, drawbacks etc. Written in a very conversational and opinionated style, I enjoyed it.
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