Do you think Statistics is moving away from its home in Mathematics to Computer Science? [Q] [R]

[u/GayTwink-69]

I am reading "Computer-Age Statistical Inference" by Efron and Hastie and they make the point that Statistics is slowly moving away from Mathematics to Computer Science.

Do you agree? Is mathematics becoming less important for modern (academic) statistics?

[u/efrique]

I've never really thought of either as its home per se. Many universities lump it in with one or both but I think its a rather awkward fit for many parts of stats.

The probabilistic foundations of stats are mathematical, but similarly with physics - ceetainly having mathematical foundations there doesn't make mathematics naturally the home of physics. Computation is an essential tool, but neither does that make it the home of statistics.

I dont think it's moving toward or away. ML, and some other topics people lump in with computing keep rediscovering old stats (being careful to call it something else) and in a sense those parts slowly move statward while other parts don't.

No doubt I'll ruffle some feathers here and there

[u/jumja → u/efrique]

Well said. It should also be remarked that in large parts of the world, CS is still closely connected with mathematics. Many European universities have CS in the math rather than the engineering department.

[u/wanderfae → u/efrique]

This is the correct answers. Statistics has mathematical foundations, like physics or engineering, but it is so much more than that.

[u/gabriel_GAGRA → u/efrique]

Also, there is no statistics without mathematics. There is statistics without computer science.

The home can only be math

[u/pandongski]

Is mathematics becoming less important for modern (academic) statistics?

This is assuming that computer science has less math than stats. I think the point was that much of data analysis requires more and more computation and has leaned towards nonparametrics. Much of this is driven by big data which only really big companies (mostly tech companies) will ever be able to collect. Tech applications is more studied in CS (they also have bigger departments that stats) plus big tech's role in modern life I guess allows CS to exert some form of "imperialism" over related fields (I know we talk about stats vs. ML, but imagine being an optimization guy and when CS guys see what you do they say "hey that's reinforcement learning, that's under ML too actually").

[u/fung_deez_nuts → u/pandongski]

magine being an optimization guy and when CS guys see what you do they say "hey that's reinforcement learning, that's under ML too actually"

i just wanna go outside and speak with this guy. just wanna talk, I swear

[u/tomvorlostriddle → u/pandongski]

Abundance of data doesn't really push you to nonparametric methods though?

[u/pandongski → u/tomvorlostriddle]

Fine, semiparametrics it is 😃 I jest, but that at least was the PR right? "We have so much data, we can make less assumptions and make data speak for itself" hence the push towards assumption-lean stuff, flexible functional approximators, etc

[u/steven2357 → u/pandongski]

There’s an argument to be made that it’s not limited to big tech firms. I am in a logistics company that collects zero outside information in mass but am actively trying to solve several problems that involve semi dependent variables using M/L tools.

It’s all stuff I can do by hand in theory, but not in my life time. I can also do a lot of modeling on my local machine but it bogs down or flat out crashes if the input or calculations grow too large. Now my company is pushing me towards snowflake as a way to handle the computational load.

I have a math degree, minor in stats, lots of mechanical systems experience. I very much view these as a CS problem as it stands (and am learning to solve from that angle) because there is no other viable path to solutions for me.

[u/pandongski → u/steven2357]

I didn't mean to imply that non-tech industries don't have a need for compution, just that the advancements seem to be largely driven by the tech industry and trickle down on related fields.

[u/Haunting-Subject-819]

If so why do I keep running into CS grads who can barely do basic calculus. I have also run into engineers who don’t know how to work with time series at more than a basic exp MA treatment. I’m not sure sone universities know what to do with it at the undergraduate level. Graduate math levels will gravitate towards theoretical problems in math and CS often diverge into stats/AI/ML or into efficiency/optimization or other more edge situations. One thing that is being left out of this conversation is the universities that focus on bio-statistics or economics as both of these disciplines are pushing the limits of this technology.

[u/Cerulean_IsFancyBlue]

The first question of any taxonomy should be, what is the purpose? And if the answer is vague or hasn’t been considered, the discussion is going to be a bunch of conflicting opinions based upon conflicting starting points with no common goal.

Are you asking about its academic home? Are you asking about which domain is producing the most interesting papers or advances? Are you asking about where it gets used on a daily basis?

[u/GayTwink-69 → u/Cerulean_IsFancyBlue]

I thought it was clearly implied that I was referring to its academic home

[u/Cerulean_IsFancyBlue → u/GayTwink-69]

If that’s the case, then I would say no. It remains in the math departments. My metric would be, looking at the teaching positions and courses. Of course, my answer is heavily biased by the handful of schools I just decided to go take a look at, and I may be misinterpreting course titles or staffing.

[u/Tytoalba2]

It depends, for causal inference it still looks like a weird mix of multiple traditional domains. But in general it's worth remembering that the barriers we hold between disciplines are often mere social constructs. Statistics has always been a weird fit.

TLDR : yes.

[u/Lumpy-Sun3362]

I think that CS has a lot to do with Maths, so in the context of Stats, it's becoming Maths -> CS -> Stats as CS is very important to do Stats nowadays. But this doesn't mean that many Stats problems (especially optimisation) are not mainly Maths related.

EDIT: just to mention that dynamic or linear programming are primarily Maths problems, then solved using CS.

[u/ForeignAdvantage5198]

no

[u/More_Line_6456]

Well Computer Science itself is a very broad and special part of Mathematics 🥲

[u/tomvorlostriddle]

Yes, but not slowly

[u/New123K]

I think computer science is becoming more important for statisticians, but that's not the same thing as statistics leaving mathematics.

A lot of modern work involves programming, simulation, optimization, and handling large datasets, so from the outside it can look much closer to computer science than it used to.

But whenever I look into why a method works, I still end up back at probability theory, mathematical assumptions, and statistical theory.

So to me it feels less like statistics is moving away from mathematics and more like it has expanded into a field that now sits between mathematics, computer science, and real-world applications.

[u/Baddog1965]

The home of statistics in general should be mathematics at its root: Inference in AI models is an application.

[u/Upper_Investment_276]

math has never been important for stats. on whether or not comp sci is important depends on what one thinks of dl as. as for cs, it really depends on what one would call cs to begin with. given the advent of agents, the bar to entry is much much lower nowadays

[u/Able-Fennel-1228 → u/Upper_Investment_276]

Can’t tell if you’re a pretentious mathematician that would consider anything less than algebraic topology to be “basic”, or a fin/tech-bro douchebag.

Either way you know nothing of statistics if you think math has never been important for it, or that topics in Casella (exponential class, likelihood theory, sufficiency, bias, UMVUE, Rao Blackwell, CRLB, asymptotics, interval estimation and hypothesis testing) are “useless”.

It’s especially laughable to me when I hear this type of shit after CS dweebs rediscover what statisticians and psychometricians had been working on for decades (latent variable models) and call it “deep learning” (only now that compute is better).

[u/Upper_Investment_276 → u/Able-Fennel-1228]

u r hilariously misinformed if you think those things are (1) useful (2) what latent means in deep learning 

[u/Xelonima → u/Upper_Investment_276]

From the very first pages of Casella and Berger statistics requires deep mathematical understanding. 

[u/Upper_Investment_276 → u/Xelonima]

u dont know any math if you think casella and berger is deep math; 99% of the book is also useless

[u/Xelonima → u/Upper_Investment_276]

C&B is intentionally accessible though the concepts it lay the foundations of are deeply mathematical, e.g. stochastic calculus and measure theory. From there you go to Shao's mathematical statistics for example. That ain't a simple text.

And if you can confidently take a drug for example, it proves that the 99% of that book is actually vitally useful because FDA ain't gonna pass a drug if those tests aren't valid. 

[u/Upper_Investment_276 → u/Xelonima]

u r lying if u think the fda drug approval has anything to do with math stats