For me personally statistics is intuitive if illustrated properly. A great example is https://seeing-theory.brown.edu/
I was 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/help people understand statistics, what resources would you consider?
At some point in my professional career I started reading non-fiction books and even bought a used statistics textbook for 10 bucks on abebooks. I didn't end up actually reading it until 12 years later during the COVID lockdown. I ended up shooting for 10 pages a day, 7 days a week. If those 10 pages included review exercises, it would be a long night.
Could just be the right book at the right time, but this one really helped me understand stuff beyond the normal HS math stuff, like RMS-error, calculating correlation, the difference between standard error and standard deviation, the relationship between sample size and standard error, t-tests, and chi-squared. Working as an SRE/release engineer, this stuff really helped me overcome a lot of _bad_ canary data analysis my predecessors had constructed.
That book was the 3rd edition of Statistics by Freedman et al.[1] One thing I want to complement was getting the pedagogy right. Most chapters have strong narrative hooks, several "check your knowledge" problems, review exercises, and post chapter bullet points to assist with spaced repetition. There's even a series of "special" review exercises covering entire sections of the book, ie exams.
For the HN crowd I should also probably note that the book is almost entirely non-bayesian and not intended to prepare readers for further coursework. You will not learn normal phraseology like "IID," "random variable" or "kernel".
[1]: https://www.amazon.com/dp/B00SLB5Q72?lv=shuf&channelId=520&p...
Modelling distributions explicitly sounds nice, yes.
If you are not doing something crazy, most reasonable people would agree with your judgement. Conversely, if you are making non-obvious inferences where reasonable people disagree, you are in murky water and no sophisticated statistical method will save you. Math is not magic; theorems merely recycle (launder) modeling assumptions into results.
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.
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.
Crap jokes aside, I mostly use Statistics In A Nutshell. It’s pretty ok. I say that as a member of the RSS for 30 odd years.
This is widely regarded as the most accessible intro textbook to Bayesian statistics.
https://xcelab.net/rm/
I read a lot of informational things, but math / stats / software has always felt like an area where a book is just the wrong format.
If I were you I'd make an interactive website like SQLZoo or a video series like StatQuest.
Those are educational formats that really clicked with me for whatever reason.
Another exemple of a successful visual pedagogical content is: https://www.byhand.ai/
But beware of opinions.
Don’t let people put you down, especially here in HN, where people are perceived to smart. Smart doesn’t equal sensible or unbiased.
Many books are written to scratch the itch of the author. Just like an open source project. It is a work of love.
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.
If you could do something similar for bayesian statistics I think that would be useful, but not necessarily popular.
However going the 'visual' route might be enough for me to pick it up.
Can you write one that's more worth reading than the standard ones? Don't answer that question, just prove it.
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.
Good luck if you do this.
However the link you have provided is not the way; it is low on content and high on pretty distractions. Use all sorts of diagrams and graphs primarily, with animations only where required. The key is to always relate to something in the real world so one can see its actual relevance. Also tie it back to other fields of mathematics so one can see how they all come together.
A good example to study is How to Measure Anything: Finding the Value of Intangibles in Business by Douglas Hubbard. Detailed review at - https://www.lesswrong.com/posts/ybYBCK9D7MZCcdArB/how-to-mea...
And of course Nassim Taleb's works are a good source of inspiration. Here is a great video summarizing Taleb's ideas nicely Pareto, Power Laws, and Fat Tails - https://www.youtube.com/watch?v=Wcqt49dXtm8