41–50 of 70 posts
I like Statistical Rethinking, but unfortunately the solver was very slow and I didn't end up using it much - not the author's fault, it's probably my computer that's way too old.
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" :)
Ready the theory is nice but you only learn by solving problems that uses it.
Earlier quoted context omitted.
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 a…
Back in my day, the 3 books for programmers were Knuth vol 1, Knuth vol 2, and Knuth vol 3. ;)
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 mu…
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 a…
[1]: https://diataxis.fr/
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How do you make 'reasonable judgements'? How do you tell whether someone else made reasonable judgements? How do you judge other people's intuition? Modelling distributions explicitly sounds nice, yes.
Look at the histogram and think about what distribution one could reasonably impute from samples. And what you would set as bounds for "outliers", per your needs. While we're at it, let me also say that it might be useful to specify outlier bounds not just based on the spread in sample values, but the costs/payoffs they imply for your application. If you are not doing something crazy, most reasonable people would agr…
Only people with prior education/training in statistics are capable of doing this. The people who don't need a textbook.
Something like 60% of US adults read at or below the 6th grade level, and 25% of US adults struggle to comprehend graphs or charts entirely. Someone who has no idea what a standard deviation is can't intuit about distributions. I think you're dramatically overestimating the average person.
Where would your book fit into this?
Earlier quoted context omitted.
How do you make 'reasonable judgements'? How do you tell whether someone else made reasonable judgements? How do you judge other people's intuition? Modelling distributions explicitly sounds nice, yes.
Look at the histogram and think about what distribution one could reasonably impute from samples. And what you would set as bounds for "outliers", per your needs. While we're at it, let me also say that it might be useful to specify outlier bounds not just based on the spread in sample values, but the costs/payoffs they imply for your application. If you are not doing something crazy, most reasonable people would agr…
Earlier quoted context omitted.
Look at the histogram and think about what distribution one could reasonably impute from samples. And what you would set as bounds for "outliers", per your needs. While we're at it, let me also say that it might be useful to specify outlier bounds not just based on the spread in sample values, but the costs/payoffs they imply for your application. If you are not doing something crazy, most reasonable people would agr…
>Look at the histogram and think about what distribution one could reasonably impute from samples. And what you would set as bounds for "outliers", per your needs. While we're at it, let me also say that it might be useful to specify outlier bounds not just based on the spread in sample values, but the costs/payoffs they imply for your application. Only people with prior education/training in statistics are capable o…
I disagree vehemently with this claim. I could cite my experience in teaching this topic to liberal arts / humanities college students in the US, but it is really more obvious than that. Anyone can understand a histogram easily and far more intuitively than they can understand the formula for a standard deviation and whether it must divide by N or N-1. Statistics courses and textbooks get stuck on that kind of pedantry, and most students end up missing the forest for the trees.
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…