Message from @realz
Discord ID: 776151786934763550
I don't speak math very well not sure if I'm communicating that well.
@RobertGrulerEsq The brief teaching I had in my different forensics class Benford's Law can be used on small data sets that have enough number cycles i.e. sufficient use of 1-9
benford's law test, with 69 (number of counties in PA) samples, (random) normally distributed between 0 and 19000000
PA*
That's pretty amazing you can do that so quickly, I'm jealous of your skills.
I had it ready lol
I just changed the number
Have you run any that have a bell-curve distribution that look like Biden's?
to be clear this is not run on real data
oh
normal distribution is a bell curve
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
(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