The math that explains why bell curves are everywhere
quantamagazine.org
The math that explains why bell curves are everywhere
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Re: The math that explains why bell curves are everywhere
#2I hate Quanta a lot
a vast amount of fluff for less than a college statistics professor would (hopefully) be able to impart with a chalkboard in 10 minutes, when Quanta has the ability to prepare animated diagrams like 3Blue1Brown but chooses not to use it
they could go down myriad paths, like how it provides that random walks on square lattices are asymptotically isotropic, or give any other simple easy-to-understand applications (like getting an asymptotic on the expected # of rolls of an n-sided die before the first reoccurring face) or explain what a normal distribution is, but they only want to tell a story to convey a feeling
they are a blight upon this world for not using their opportunity to further public engagement in a meaningful way
Re: The math that explains why bell curves are everywhere
#3Great article. Personally I have been learning more about the mathematics of beyond-CLT scenarios (fat tails, infinite variance etc)
The great philosophical question is why CLT applies so universally. The article explains it well as a consequence of the averaging process.
Alternatively, I’ve read that natural processes tend to exhibit Gaussian behaviour because there is a tendency towards equilibrium: forces, homeostasis, central potentials and so on and this equilibrium drives the measurable into the central region.
For processes such as prices in financial markets, with complicated feedback loops and reflexivity (in the Soros sense) the probability mass tends to ends up in the non central region, where the CLT does not apply.
Re: The math that explains why bell curves are everywhere
#4Great article. Personally I have been learning more about the mathematics of beyond-CLT scenarios (fat tails, infinite variance etc) The great philosophical question is why CLT applies so universally. The article explains it well as a consequence of the averaging process. Alternatively, I’ve read that natural processes tend to exhibit Gaussian behaviour because there is a tendency towards equilibrium: forces, homeost…
As to ye philosophy of “why” the CLT gives you normals, my hunch is that it’s because there’s some connection between:
a) the CLT requires samples drawn from a distribution with finite mean and variance
and b) the Gaussian is the maximum entropy distribution for a particular mean and variance
I’d be curious about what happens if you starting making assumptions about higher order moments in the distro
Re: The math that explains why bell curves are everywhere
#5Great article. Personally I have been learning more about the mathematics of beyond-CLT scenarios (fat tails, infinite variance etc) The great philosophical question is why CLT applies so universally. The article explains it well as a consequence of the averaging process. Alternatively, I’ve read that natural processes tend to exhibit Gaussian behaviour because there is a tendency towards equilibrium: forces, homeost…
You (and others) may enjoy going down the rabbit hole of universality. Terence Tao has a nice survey article on this which might be a good place to start: https://direct.mit.edu/daed/article/141/3/23/27037/E-pluribu...
Re: The math that explains why bell curves are everywhere
#6I hate Quanta a lot a vast amount of fluff for less than a college statistics professor would (hopefully) be able to impart with a chalkboard in 10 minutes, when Quanta has the ability to prepare animated diagrams like 3Blue1Brown but chooses not to use it they could go down myriad paths, like how it provides that random walks on square lattices are asymptotically isotropic, or give any other simple easy-to-understan…
A lot of times on HN when a math topic comes up that isn't about 3b1b, someone will jump in to say "this isn't as good as 3b1b". Last time I saw that, I was moved to comment:
https://news.ycombinator.com/item?id=45800657
3b1b doesn't have the same goal as Quanta, or as introductory guides. It's actually not that great a teaching tool (it's truly great at what it is for, which is (a) appreciation and motivation, and (b) allowing people to signal how smart they are on message board threads by talking about how much people would get out of watching 3b1b).
This is prose writing about math. It's something you're meant to read for enjoyment. If you don't enjoy it, fine; I don't enjoy cowboy fiction. So I don't read it. I don't so much look for opportunities to yell at how much I hate "The Ballad of Easy Breezy".
Re: The math that explains why bell curves are everywhere
#7Hot take: bell curves are everywhere exactly because the math is simple.
The causal chain is: the math is simple -> teachers teach simple things -> students learn what they're taught -> we see the world in terms of concepts we've learned.
The central limit theorem generalizes beyond simple math to hard math: Levy alpha stable distributions when variance is not finite, the Fisher-Tippett-Gnedenko theorem and Gumbel/Fréchet/Weibull distributions regarding extreme values. Those curves are also everwhere, but we don't see them because we weren't taught them because the math is tough.
Re: The math that explains why bell curves are everywhere
#8I hate Quanta a lot a vast amount of fluff for less than a college statistics professor would (hopefully) be able to impart with a chalkboard in 10 minutes, when Quanta has the ability to prepare animated diagrams like 3Blue1Brown but chooses not to use it they could go down myriad paths, like how it provides that random walks on square lattices are asymptotically isotropic, or give any other simple easy-to-understan…
A lot of times on HN when a math topic comes up that isn't about 3b1b, someone will jump in to say "this isn't as good as 3b1b". Last time I saw that, I was moved to comment: https://news.ycombinator.com/item?id=45800657 3b1b doesn't have the same goal as Quanta, or as introductory guides. It's actually not that great a teaching tool (it's truly great at what it is for, which is (a) appreciation and motivation, and (…
I don’t fault Quanta (or 3b1b) for being the way they are. Each is serving their goal audience pretty well.
My compliant is only that there should be a dozen more just like them, each competing with each other for the best, most engaging math and science content. This would allow for more a broader audience skillevel to be reached.
As it stands, we’re lucky even to have Quanta and 3b1b.
I think there is hope though, quite a few new-ish creators on YouTube are following in Grant’s footsteps and producing very technically detailed and informative content at similar quality levels.
Re: The math that explains why bell curves are everywhere
#9Great article. Personally I have been learning more about the mathematics of beyond-CLT scenarios (fat tails, infinite variance etc) The great philosophical question is why CLT applies so universally. The article explains it well as a consequence of the averaging process. Alternatively, I’ve read that natural processes tend to exhibit Gaussian behaviour because there is a tendency towards equilibrium: forces, homeost…
As to ye philosophy of “why” the CLT gives you normals, my hunch is that it’s because there’s some connection between: a) the CLT requires samples drawn from a distribution with finite mean and variance and b) the Gaussian is the maximum entropy distribution for a particular mean and variance I’d be curious about what happens if you starting making assumptions about higher order moments in the distro
IIRC there's a video by 3b1b that talks about that, and it is important that gaussians are closed under convolution.
Re: The math that explains why bell curves are everywhere
#10Hot take: bell curves are everywhere exactly because the math is simple. The causal chain is: the math is simple -> teachers teach simple things -> students learn what they're taught -> we see the world in terms of concepts we've learned. The central limit theorem generalizes beyond simple math to hard math: Levy alpha stable distributions when variance is not finite, the Fisher-Tippett-Gnedenko theorem and Gumbel/Fr…
That’s exactly the right take and the article proves it:
Statisticians love averages so everywhere that could be sampled as a normal distribution will be presented as one
The median is actually more descriptive and power law is equally as pervasive if not more