it isn't exactly the same as Biden's bell curve though

it is a bell curve centered on zero (I abs() the negative numbers)

I can center it on some number though

I see. Just wondering if adjusting of the sample size adjusted the curve more in alignment with the thumbnail in the math guy's video above of Biden's numbers.

oh I see

no, increasing the sample size for my experiment makes it more like benford's law

@realz Correct me if I am wrong here, but in using Benford distribution as evidence of voter fraud, you would have to have representative sample sizes from an election verified to be true (baseline) and one that has been confirmed as fraud, and calculate probability based on how much the current election approach the latter, yes?

probably because I center around 0

@Doc that is another good point; to test previous elections for the same thing

(but there are those that might say "it's always been going on")

Right, so you need some sort of baseline.

well that is _yet_ another litmus test for these proofs

because all elections will have some systematic and random defects.

my experiment just shows that with few samples, the expected error for each bar is too high

right

very nice.

Sorry for interrupting, I just liked your number-dabbling.

@RobertGrulerEsq I suspect the fact that I center around 0 is why I don't get he same results as the math guy's video

my orders of magnitude are much large as well

a uniform random generator also works, with the weird artifact that it very much depends on what your maximum number is

with a maximum number of say 20000000, almost half your numbers will start with 1

more than half

that's why I decided to use some other distribution

I have to rewatch Stand-up Maths video, but I think his point was more about the small range of magnitude of the numbers rather than the shape of the distribution (a normal distribution is typical, and would probably usually result in a nice Benford's law)

the point of the normal distribution is just that with such a small range of magnitudes, you get to see the distribution of the data in the Benford's law graph

OK so I actually _do_ see the distribution peak out if I lower the magnitudes

`d3.randomNormal(50000,5000)`

so centered around 50k, with a sigma of 5000

naturally most numbers start with 5

some numbers start with 4

and 6

lol it might even be fun to make sliders for this

Wow yea that's the similar distribution. Very interesting.

OK so basically if you have a small sigma, you will be able to "see" your distribution in the Benford's law graph

I made sliders

I guess I can just make this public for anyone to play with

https://observablehq.com/@realazthat/benfords-law/2

enjoy 😄

just play with the slidy sliders

I took a class in Visual Basics probably 12-14 years ago (poorly) and that's as far as I went. So this is mind blowing to me 😆