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.
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/
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.
I would at least investigate it. I have purchased and read statistics books recently. Mostly the old classics by R.A. Fisher and D.R. Cox and etc, I have a copy of Handbook 91 by Mary Natrella. My questions are pretty simple. I like the "worked examples" approach in Handbook 91.
Not sure I'd read it as-written. But I'd love a statistics book whereby the chapters are real case studies. I personally learn best when I can apply new theory to a tangible problem (not just an example problem that's been reduced to almost nothing).
However going the 'visual' route might be enough for me to pick it up.
Yes, there is always a need for another "intuitive statistics" book.
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.
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
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.
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.
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.
Back in my day, the 3 books for programmers were Knuth vol 1, Knuth vol 2, and Knuth vol 3. ;)
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/
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.
I would at least investigate it. I have purchased and read statistics books recently. Mostly the old classics by R.A. Fisher and D.R. Cox and etc, I have a copy of Handbook 91 by Mary Natrella. My questions are pretty simple. I like the "worked examples" approach in Handbook 91.
Not sure I'd read it as-written. But I'd love a statistics book whereby the chapters are real case studies. I personally learn best when I can apply new theory to a tangible problem (not just an example problem that's been reduced to almost nothing).
However going the 'visual' route might be enough for me to pick it up.
I'm a nerd and do a lot of stats for my job and I would not read a statistics textbook.
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.
Yes, with the caveat that someone else with knowledge in the field has to recommend it to me. Partner up with someone who teaches statistics.
I would! If you are thinking to write one, do it!
Yes, I love it and would do so
https://stewartschultz.com/statistics/books/Statistics_Terri...
Such a bizarre question. Many, many people have read statistics textbooks.
Can you write one that's more worth reading than the standard ones? Don't answer that question, just prove it.
Yes, there is always a need for another "intuitive statistics" book.
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
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.
Probably.