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Introduction to Modern Statistics

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Re: Introduction to Modern Statistics

#31

Post by usgroup »

iTokio wrote: post #27
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…

There is also Brillant that has a very polished interactive course: https://brilliant.org/courses/statistics/

These things are great if they add value for you, but I would be very skeptical of any non-mathematical approach to statistics. I think statistics is only made clear by mathematics, much the same as Physics. And one cannot grasp statistics without being able to understand the maths.

I think that still the best way to understand statistics is to start with the mathematical theory and to grind 1000+ textbook problems.

Re: Introduction to Modern Statistics

#32

Post by NeutralForest »

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.

Re: Introduction to Modern Statistics

#33

Post by Ensorceled »

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…

It's ironic that this ... rant? ... is basically unreadable without knowledge of basic statistical methods.

How do you teach any of this to someone who hasn't already taken introductory statistics? How do you learn anything if you first have to learn the myriad ways something you don't even have a basic working knowledge of can fail before you learn it?

Re: Introduction to Modern Statistics

#34

Post by ImaCake »

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…

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 this, but teaching students that they can use regression for most of the problems they would have used seemingly arcane tests for is going to be much more useful for the students.

Here is a lovely page demonstrating how to do this in R: https://lindeloev.github.io/tests-as-linear/

Re: Introduction to Modern Statistics

#35

Post by ImaCake »

dereify wrote: post #15
d00mer wrote: post #9

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

fyi many state-of-the-art statistical libraries exist (or are properly maintained) in R only

I find it depends on what you want. There is no canonical GAM (gen. addative model) library in python but there are a few options - which are not easy to use. The statsmodels GAM implementation appears to be broken. R, of course, has a stupid easy to use GAM library that is pretty fast.

On the other hand, R has too many obscure options for what I can find in scipy or sklearn. So I find it easier to just jump into sklearn, use the very nice unified interface "pipelines" to churn through a whole bunch of different estimators without having to do any munging on my data.

So I think it just depends on your field. But R seems to stick more with academia.

Re: Introduction to Modern Statistics

#36

Post by mkl »

usgroup wrote: post #31
iTokio wrote: post #27

Earlier quoted context omitted.

There is also Brillant that has a very polished interactive course: https://brilliant.org/courses/statistics/

These things are great if they add value for you, but I would be very skeptical of any non-mathematical approach to statistics. I think statistics is only made clear by mathematics, much the same as Physics. And one cannot grasp statistics without being able to understand the maths. I think that still the best way to understand statistics is to start with the mathematical theory and to grind 1000+ textbook problems.

> I think that still the best way to understand statistics is to start with the mathematical theory and to grind 1000+ textbook problems.

Are there any books you'd recommend for this approach?

Re: Introduction to Modern Statistics

#37

Post by mjburgess »

Ensorceled wrote: post #33
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…

It's ironic that this ... rant? ... is basically unreadable without knowledge of basic statistical methods. How do you teach any of this to someone who hasn't already taken introductory statistics? How do you learn anything if you first have to learn the myriad ways something you don't even have a basic working knowledge of can fail before you learn it?

The comment is addressed to the informed reader who is the only one with a hope of being persuaded on this point.

To teach this, from scratch, I think is fairly easy -- but there's few with any incentive to do it. Many in academia wouldnt know how, and if they did, would discover that much of their research can be shown a priori to not be worthwhile (rather than after a decade of 'debate').

All you really need is to start with establishing an intuitive understanding of randomness, how apparently highly patterned it is, and so on. Then ask: how easy is it to reproduce an observed pattern with (simulated) randomness?

That question alone, properly supported via basic programming simulations, will take you extremely far. Indeed, the answer to it is often obvious -- a trivial program.

That few ever write such programs shows how the whole edifice of stats education is geared towards confirmation bias.

Before computers, stats was either an extremely mathematical disipline seeking (empirically useless) formula for toy models; or using heuristic empirical formula that rarely applied.

Computers basically obviate all of that. Stats is mostly about counting things and making comparisons -- perfect tasks for machines. with only a few high-school mathematical formula most could derive most useful statistical techniques as simple computer programs.

Re: Introduction to Modern Statistics

#38

Post by noelwelsh »

mjburgess wrote: post #37
Ensorceled wrote: post #33

Earlier quoted context omitted.

It's ironic that this ... rant? ... is basically unreadable without knowledge of basic statistical methods. How do you teach any of this to someone who hasn't already taken introductory statistics? How do you learn anything if you first have to learn the myriad ways something you don't even have a basic working knowledge of can fail before you learn it?

The comment is addressed to the informed reader who is the only one with a hope of being persuaded on this point. To teach this, from scratch, I think is fairly easy -- but there's few with any incentive to do it. Many in academia wouldnt know how, and if they did, would discover that much of their research can be shown a priori to not be worthwhile (rather than after a decade of 'debate'). All you really need is to…

The modern approach, of which this textbook is an example, does start with simulation. In fact there is very little classical statistics (distributions, analytic tests) in the book. The Berkeley Data 8 book, which I link to in another comment, takes the same approach. I imagine there is still too much classical material for your tastes, but there is definitely change happening.

Re: Introduction to Modern Statistics

#39

Post by taeric »

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…

Model building, at large, is the thing I regret being bad at. Model your problem and then throw inputs at it and see what you can see.

Sucks, as we seem to have taught everyone that statistical models are somehow unique models that can only be made to get a prediction. To the point that we seem to have hard delineations between "predictive" models and other "models.".

I suspect there are some decent ontologies there. But, at large, I regret that so many won't try to build a model.

Re: Introduction to Modern Statistics

#40

Post by dr_dshiv »

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…

Do you know of any validation studies with Advanced Data Analysis (formerly code interpreter) in chatGPT? I think it can be excellent as a teaching tool.
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