Ask HN: Would you read a statistics textbook?
I had a classic Bayesian statistical education throughout my undergraduate and grad years, and I've come to conclude that the plethora of books offered are pretty bad. For me personally, statistics is intuitive if illustrated properly, like the website Seeing Theory. I'm wondering whether it would make sense to turn the intuitions behind statistics into a book. Would anyone read it? Do people still read stats books, or would it be mostly a waste of effort? If you wanted to teach or help people understand statistics, what resources would you consider?
No idea about statistics, but in most physics courses in my university, they recommend 3 books: 1) The main book, that has a complete explanation and is well ordered. It's for learning. 2) The Landau book, that is super short and hard. It's only to check you didn't miss any important formula or topic. 3) The Feynman book, that is an assorted collection of fairytales for physicists. It's a pleasure to read it but you must already read 1 to understand it. 4) The Schaum book, that is almost a collection of exercises. Some people hate it. Some people love it. I like it as a companion to the other books. I guess you are complaining that 1 is boring and want to write 3. It's a good idea, but it's harder than expected.
Similar to an old idea I had about how every programming language needs three books: 1. Basic introduction. 2. Reference tome, which has absolutely everything. 3. Cookbook with style advice for the more advanced student, which assumes you've read 1 and can look up various details in 2. These days, 2 would be a wiki and 1 would likely be a bunch of pages on that wiki, but it's still good if you have someone sit down and write 3.
Having also studied statistics in university (undergrad), something I kept running into is that you can't really unlock the intuition for many concepts without taking more advanced courses. For example, degrees of freedom shows up as early as AP Statistics, but even a non-rigorous visual explanation of it leans on linear algebra, which most students don't see until much later. I think more resources like seeing-theory would be great since stats books are almost universally dry (Blitzstein being a notable exception), but I'm not sure how easily more advanced concepts lend themselves to visual explanation in a way that's digestible for a non-stats person.
I feel this boils down my learning journey as well. You start unraveling a very good intuition about the underlying concepts MUCH much later, but partly because those intuitions themselves are never conveyed and are supposed to be learned from the proofs, and are an indirect product of learning.
It depends on what parts of statistics is being taught and the application of each of the leanings and how it relates to the real world. More generally, I would buy a statistics if it is linked to today's interesting technological breakthroughs and also if it comes as a distilled version for beginners.
- a_bonobo
Did you see that Andrew Gelman and others just published Bayesian Workflows? https://avehtari.github.io/Bayesian-Workflow/
It sounds similar to what you're after, away from describing the logic of models and the maths, instead it's about (quote from intro) 'There are all sorts of tacit knowledge in applied statistics that do not make it into published
papers and textbooks. The present book is intended to put some of these ideas out in the open'
Where would your book fit into this?
- ssivark
My personal opinion is that statistics textbooks usually come from a prescriptive perspective, and that makes it challenging for the reader to get visceral intuition for what is actually going on. Any reader would be far better off just visualizing the damn distribution / samples and using reasonable judgement, instead of implicitly assuming a Gaussians distribution and blindly memorizing tests / formulae. Making the distributions explicit allows us to model them and get an intuition for what the samples are telling us. I would whole-heartedly recommend the Model based machine learning book to anyone (online version is free) https://mbmlbook.com/
- gus_massa
No idea about statistics, but in most physict courses in my university, they recomend 3 books:
1) The main book, that has a complete explanation and is well ordered. It's for learning.
2) Tha Landau book, that is super short and hard. It's only to check you didn't miss any important formula or topic.
3) There Feynman book, that is anassorted colection of fairytales for physicist. It's a pleasure to read it but you must already read 1 to understand it.
4) The Shaum book, that is almost a colection of exercices. Some people hate it. Some people love it. I like it as a companion to theother books.
I guess you are complaining that 1 is boring and want to write 3. It's a good idea, but it's harder than expected.
- actualeff0rt
I've got a Bachelors and two Masters degrees in CS/Math, but yet I feel Probability and Statistics is my greatest weakness. I just cannot grok it / build an intuition for it, and believe me, I've tried. My biggest gripe with Prob/Stats textbooks is that it's very hard to explain things without needing to rely on measure theory.
Maybe probability and statistics are a skill issue on my behalf, but what I absolutely loathe is the absolute lack of standardisation when it comes to notation in measure theory. Every textbook does it differently. All of them assume that their notation is the one everyone uses. Nobody bothers to explain _what_ the notation means. If you ask me, every bit of new notation should be introduced with a sentence or two on "how to read this symbol in your head" - especially when there are indices, subscripts and superscripts involved. It's especially terrible for measure theory because there's so much "implicit" information you're supposed to gather from the context - but in a way I understand it, because if every bit of notation of absolute and complete, I imagine it would be quite hard to type up.
Anyways, my rant on measure theory notation aside - I would absolutely read yet another Prob/Stats textbook. But unfortunately I will also drop it really quickly if the author doesn't show me any "notation-sympathy" :)
- montalbano
How would it overlap or differ from 'Statistical Rethinking'?
This is widely regarded as the most accessible intro textbook to Bayesian statistics.