Statistics Done Wrong
by Alex Reinhart · first published 2015
89% recommend11% neutral
Why HN recommends it
Short, example-filled tour of common statistical mistakes and the assumptions behind methods like t-tests, ANOVA and regression; suits anyone doing stats work, from beginners to practitioners reading papers.
Summarised by Claude from recommending comments
Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong. Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your p
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…is widely applicable. Brilliant introductory for everyone who wants to do something stats related. It's amazing how much can be done with no fancy deep learning algorithms, just plain simple stats. - Statistics Done Wrong by Alex Reinhart. Plenty of gotchas with real world examples from academia. Well written and easy to read. - The Circle by Dave Eggers. This one was scary. About imaginary corporation (a blend of Facebook…
on “Ask HN: Books you read in 2016?” ↗…find a relatively small effect, but the layperson making decisions (do I allow right turn on red or is that dangerous?) may not get that nuance. A book that really helped clarify my thinking on this is _Statistics Done Wrong_ by Alex Reinhart. Edit: remove "interpret" from last sentence to clarify
on “Scientists rise up against statistical significance” ↗…tricky to get right, and plenty of papers contain errors and omissions, and I'm not necessarily saying that's the case here, but one ought to be aware. More on this topic, in case anyone is interested – Statistics Done Wrong [2] [1] https://www.bls.gov/ncs/ncbr0002.pdf [2] https://www.amazon.com/gp/product/1593276206
on “Kickstarter’s staff is unionizing” ↗Not exactly a textbook, but good to read nonetheless is, "Statistics Done Wrong"[1]. It is a short read but is filled with all the most common ways in which statistical analysis is abused with concrete examples. [1] Statistics Done Wrong: The Woefully Complete Guide https://www.amazon.com/dp/1593276206/ref=cm_sw_r_cp_apa_i_u....
on “The Little Handbook of Statistical Practice (2012)” ↗I would love to see a book like Alex Reinhart's Statistics Done Wrong make these lists more often. It's not focused on data science specifically. But it's short, sweet, and just might leave you better prepared to tell whether the emperor is wearing actual clothes, or if…
on “Best Data Science Books According to the Experts” ↗== Vim or Emacs == Just pick one and force yourself to use it to the exclusion of other editors. Future you will thank you later, because you'll still be using it 20 years from now. "We are typists first, programmers second" comes to mind. You need to be able to move chunks of code around, substitute things with regexes, use marks, use editor macros, etc. == 6.824: Distributed Systems == htt
on “Ask HN: What source code is worth studying? ” ↗Well, if you get 30 persons to flip a coin six time, there is good chance one of them will get all tails or all head. Now ask him his strategy for such amazing coin flipping skills! (example taken from the book "statistics done wrong"[1]). I would be interested in seeing a total distribution of hedge funds return, not just outliers. [1]:https://www.statisticsdonewrong.com/
on “I've reproduced 130 research papers about “predicting the stock market”” ↗I enjoy Statistics Done Wrong https://www.statisticsdonewrong.com
on “Why Is It Taking 20 Minutes to Mine This Bitcoin Block?” ↗A quick 101: https://www.statisticsdonewrong.com/power.html
on “Statisticians want to abandon science’s standard measure of ‘significance’” ↗Not necessarily. Variance is a powerful force. 100+ samples may sound like a lot, but it's often insufficient. My favorite book on this topic is http://www.statisticsdonewrong.com/index.html, in particular the sections on statistical power.
on “Meet the People Taking over Hacker News” ↗I think this site is really related to this topic, even if it doesn't involve term 'big data'. http://www.statisticsdonewrong.com/
on “Big data: are we making a big mistake?” ↗Some other good ones are http://www.badscience.net/ and http://www.statisticsdonewrong.com/index.html (I suppose both of those links are relevant versus the awfulness in this Atlantic article.)
on “Toxins That Threaten Our Brains” ↗Oh god, I missed the passage about the 400 t-tests. Your professor is the guy on the cover of this book: https://www.statisticsdonewrong.com/ If your comment was a book, I'd lovingly put it on the shelf next to Reinhart's masterpiece ("revised and expanded ... with three times as many statistical errors and examples!"). Unlike Shakespeare, it appears nonfiction is well within the capabilities of
on “Deep learning algorithm diagnoses skin cancer as well as seasoned dermatologists” ↗…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…
on “Ask HN: What language-agnostic programming books should I read?” ↗Most arguments used in this discussion are much better exposed in the book https://www.statisticsdonewrong.com/
on “Scientists rise up against statistical significance” ↗