Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Introduction to Modern Statistics

openintro-ims2.netlify.app

Post reply on HN
Print view View original

71–80 of 138 posts

Re: Introduction to Modern Statistics

#71

Post by rdhyee »

NeutralForest wrote: post #32
dleeftink wrote: post #19

Anyone looking to apply and compare frequentist and bayesian methods within a unified GUI (which is essentially an elegant wrapper to R and selected/custom statistical packages), should check out JASP developed by the University of Amsterdam [0]. It's free to use, and the graphs + captions generated during each step are publication quality right out of the box. Using it truly feels like a 'fresh way' to do statistics…

there was an interview of one of the JASP (creator or maintainer, can't remember) in the "Learn Bayesian Stats" podcast; it was very interesting.

I think the referenced episode is https://learnbayesstats.com/episode/61-why-we-still-use-non-... Thanks for pointing it out!

Re: Introduction to Modern Statistics

#72

Post by gpderetta »

ImaCake wrote: post #34
usgroup wrote: post #29

Earlier quoted context omitted.

I've had similar thoughts, but I think its more to do with what is in your head at the time you hear about it. I found permutation tests satisfying to learn about because they somehow helped consolidate what I knew from distribution theory. If I didn't know any distribution theory prior, I'm not sure they could have that effect. If you study mathematical statistics, it is not taught as a cookbook. At the elementary l…

I think those short courses would be more effective if they didn't bother with ANOVA and instead taught intro probability and distributions and then jumped straight to regression. ANOVA is just a really specific way of doing a regression. In R, and python::statsmodels you get the answer to (essentially) an ANOVA any time you run an LM or GLM; its the Z-statistic for your whole model. I know there is more nuance to th…

Stastsmodels and that github page are the only reason I have some understanding of statistical tests.

Re: Introduction to Modern Statistics

#73

Post by perrygeo »

nomilk wrote: post #18
d00mer wrote: post #9

They should remove "modern" from the title, because who the hell uses the "R programming language" these days anymore?

Before I knew command line, I tried to install python and spent the next 3 days resolving an installation issue with 'wheel'. By contrast, from first downloading R to running my first R script took about 1 hour (the most difficult part was opening the 'script' pane in RStudio IDE, which doesn't open by default on new installations, for some reason). There's huge demand out there for statistical software that's access…

R and CRAN really get package management right. Even as a very infrequent R user, there are no surprises, it "just works". Compare that to my daily Python usage where I am continually flummoxed by dependency issues.

Re: Introduction to Modern Statistics

#74

Post by 2devnull »

brutusborn wrote: post #67
2devnull wrote: post #59

Earlier quoted context omitted.

“ that much of their research can be shown a priori to not be worthwhile” Bingo. Cargo cult stats all the way down. It’s not just personal interest, it’s the entire field, it’s their colleagues, mentors, and students. Good luck getting somebody to see the light when not just their own income depends on not seeing it, their whole world depends on the “stat recipes” handed down from granny.

I think the egotistical aspect is the most powerful: many researchers have built an identity based on the fact that they “know” something, so to propose better alternatives to their pet theories is tantamount to proposing their life is a lie. To change their mind they need to admit they didn’t “know”. The better the alternatives, the more fierce the passion with which they will be rejected by the mainstream.

I now think it’s best explained by simple economics. Academia and academics are the product of economic forces by and large. It’s not quirky personalities or uniquely talented minds that make up academia today. It’s droves of conscientious (big five sense) conformists, with either high iq or mere socio-economic privilege, who have been trained by our society to feel that financial security means college, and even more financial security means even more college. Credentials are like alpha .05, they solve a scale problem in a way that alters the quality/quantity ratio. If you want more researchers/research/science output, credentials and alpha .05 cargo cult stats are your levers to get more quantity at lower quality.

Re: Introduction to Modern Statistics

#75

Post by bunderbunder »

usgroup wrote: post #29
noelwelsh wrote: post #2

Statistics education is undergoing a bit of a revolution, driven by the accessibility of computers. For example, hypothesis testing is introduced by randomization[1], using a randomized permutation test[2]. I find this really easy to understand, compared to how I learned statistics using a more traditional approach. The traditional approach taught be a cookbook of hypothesis tests to use: use the t-test in this situa…

I've had similar thoughts, but I think its more to do with what is in your head at the time you hear about it. I found permutation tests satisfying to learn about because they somehow helped consolidate what I knew from distribution theory. If I didn't know any distribution theory prior, I'm not sure they could have that effect. If you study mathematical statistics, it is not taught as a cookbook. At the elementary l…

Principles of Statistics by M.G. Bulmer is a nice introduction to the mathematical side of things. It's part of Dover's classic textbook series, so it's inexpensive compared to newer textbooks, and also concise and well-written.

It does assume you already have a solid understanding of calculus and combinatorics, though. Which I think is fair. Discrete statistics is arguably just applied combinatorics, and continuous statistics applied calculus, so if you have a strong foundation in those two subjects then you're already 90% of the way there. (And, if you don't, stop the cart and let the horse catch up.)

Re: Introduction to Modern Statistics

#77

Post by juunpp »

mjburgess wrote: post #30

What's often missing from these introductions is when statistics will not work; and what it even means when it "works". The amount of data needed to tell between two normal is about 30 data points -- between two power-law distributions, >trillion. (And this basically scuppers the central limit theorem, on which a lot of cargo-cult stats is justified). Stats, imv, should be taught simulation-first: code up your hypoth…

I am a noob and I've always got stuck on comparing two independent means. Assumption: normality. Yeah, data is never normal in my bakery.

Re: Introduction to Modern Statistics

#78

Post by haberman »

mjburgess wrote: post #30

What's often missing from these introductions is when statistics will not work; and what it even means when it "works". The amount of data needed to tell between two normal is about 30 data points -- between two power-law distributions, >trillion. (And this basically scuppers the central limit theorem, on which a lot of cargo-cult stats is justified). Stats, imv, should be taught simulation-first: code up your hypoth…

This really resonates with me. I've attempted self-study about statistics many times, each time wanting to understand the fundamental assumptions that underlie popular statistical methods. When I read the result of a poll or a a scientific study, how rigorous are the claimed results, and what false assumptions could undermine them?

I want to build intuitions for how these statistical methods even work, at a high level, before getting drowned in math about all the details. And like you say, I want to understand the boundaries: "when statistics will not work; and what it even means when it "works".

I imagine that different methodologies exist on a spectrum, where some give more reliable results, and others are more likely to be noise. I want to understand how to roughly tell the good from the bad, and how to spot common problems.

Re: Introduction to Modern Statistics

#79

Post by obscurette »

ndr wrote: post #68
obscurette wrote: post #60

Earlier quoted context omitted.

I have been a math teacher and although I can't guarantee that I didn't suck, I can say that most of kids don't develop this attitude because of teachers, but because of their parents. "My mum says that she sucked at math/music/whatever as well, so do I!" is far too common. As a teacher I just didn't have resources to influence this attitude either.

Yes, parents can be horrible too. Unfortunately it's somehow socially acceptable and even worthy of pride in some circles, to be "bad at math". It's seems very rare for someone to openly say "I'm bad at [my native language]" or "writing". I feel stats is has a somewhat similar effect even among those with math education. Several friends who have a degree in math recoil at the first mention of stats concepts.

> It's seems very rare for someone to openly say "I'm bad at [my native language]" or "writing".

It is actually even fashionable in non-english countries. Declaring "I'm bad at [my native language], I only use english anyway" makes you a better person somehow. And it's not rare in other areas either – in post-truth world it's trendy not to know things.

Re: Introduction to Modern Statistics

#80

Post by sndean »

0xDEAFBEAD wrote: post #65
mjburgess wrote: post #30

What's often missing from these introductions is when statistics will not work; and what it even means when it "works". The amount of data needed to tell between two normal is about 30 data points -- between two power-law distributions, >trillion. (And this basically scuppers the central limit theorem, on which a lot of cargo-cult stats is justified). Stats, imv, should be taught simulation-first: code up your hypoth…

>The amount of data needed to tell between two normal is about 30 data points What are you trying to say here? If there are two normal distributions, both with variance one, one having mean 0 and the other having mean 100, and I get a single sample from one of the distributions, I can guess which distribution it came from with very high confidence. Where did the number 30 come from?

> Where did the number 30 come from?

Yeah, I've also heard 30 for normal distributions over and over in ~7 stats courses that I've taken.

This SE stats answer sounds reasonable enough: https://stats.stackexchange.com/a/2542

Post reply on HN