As for why it works in nature, I think it's just the mathematical expression of the general concept "the larger, the fewer". There are always more smaller things than larger things, they tend to follow a certain distribution, and it is evident in the leading digits.
What are the counts in these graphs though? Total votes per district? So basically Biden has too many districts reporting large numbers are opposed to few?
It asserts that, if you take the log base 10, the fractional part is distributed uniformly.
An increase from a first digit of "1" to "2" is a 100% increase. An increase from "8" to "9" is only a 12.5% increase. Therefore, you should see more '1's than '8's.
For a lot of real-world numbers, things increase in percentages and not by counting.
Benford's law, also called the Newcomb–Benford law, the law of anomalous numbers, or the first-digit law, is an observation about the frequency distribution of leading digits in many real-life sets of numerical data. The law states that in many naturally occurring collections of numbers, the leading digit is likely to be small.[1] For example, in sets that obey the law, the number 1 appears as the leading significant digit about 30% of the time, while 9 appears as the leading significant digit less than 5% of the time. If the digits were distributed uniformly, they would each occur about 11.1% of the time.[2] Benford's law also makes predictions about the distribution of second digits, third digits, digit combinations, and so on.
Wow, forgot about Benford, great thinking! What’s awesome about this law is that it gives zero fucks about what the data actually represents. It’s purely concerned with the digits on the page.
It basically stems from the fact that in base 10, you expect 1 to appear in the front of a number often. Take, for example, the numbers from 0000-1999. If you pick any of those numbers at random, the “thousands” digit (if we pad out zeros) has a 50-50 chance of being a 1. The same thing applies as you keep adding digits. Using that thinking, you can come up with Benford’s distribution up above.
Now if you have a bunch of numbers that don’t obey that law of numbers, you know that they have been manipulated. This is the same as if you found a lump of pure plutonium in your backyard... you know it’s artificial because it just doesn’t happen that way in nature.
Many types of real-world data roughly correspond to the log-normal distribution or the Pareto distribution, where simply put, there are many items with a low-to-middling value, and a few items with values orders of magnitude above the others. Examples include household incomes, populations of settlements, lengths of rivers, etc.
If you took a large-enough data set of such things, and counted each value's first digit in base-10, since lower values are more likely than higher numbers, the number 1 is very likely to be the most common number, followed by the rest in a curve that can be mathematically described.
Unless of course, either the dataset isn't one of the types of data that would conform to the aforementioned distributions (unlikely, given how historical tallies do conform), or the dataset had been artificially manipulated
nice. Anyone know why Benford's law works in nature? this is the first time I have heard of this law and it is pretty cool!
Do you have the tally of Trump's numbers as well?
It's posted around here somewhere. Trump's numbers match Benford's law perfectly.
As for why it works in nature, I think it's just the mathematical expression of the general concept "the larger, the fewer". There are always more smaller things than larger things, they tend to follow a certain distribution, and it is evident in the leading digits.
What are the counts in these graphs though? Total votes per district? So basically Biden has too many districts reporting large numbers are opposed to few?
It asserts that, if you take the log base 10, the fractional part is distributed uniformly.
An increase from a first digit of "1" to "2" is a 100% increase. An increase from "8" to "9" is only a 12.5% increase. Therefore, you should see more '1's than '8's.
For a lot of real-world numbers, things increase in percentages and not by counting.
https://en.wikipedia.org/wiki/Benford%27s_law
Do you have both trump data and biden data? I wouldn't mind verifying it as well.
https://thedonald.win/p/11PpFtEjwI/bidens-vote-tallies-violate-benf/c/
Wow, forgot about Benford, great thinking! What’s awesome about this law is that it gives zero fucks about what the data actually represents. It’s purely concerned with the digits on the page.
It basically stems from the fact that in base 10, you expect 1 to appear in the front of a number often. Take, for example, the numbers from 0000-1999. If you pick any of those numbers at random, the “thousands” digit (if we pad out zeros) has a 50-50 chance of being a 1. The same thing applies as you keep adding digits. Using that thinking, you can come up with Benford’s distribution up above.
Now if you have a bunch of numbers that don’t obey that law of numbers, you know that they have been manipulated. This is the same as if you found a lump of pure plutonium in your backyard... you know it’s artificial because it just doesn’t happen that way in nature.
So did the Dems!
I did it here as well:
https://thedonald.win/p/11PpFxnhKi/verification-of-michigan-electio/ Also included the raw file for you to look over. I used originlab to analyze the frequency counts.
I... don't understand the two graphs.
can someone explain that to me? what are they showing (whats the scale below, and how does it prove?
If this is true - we need to SHOW. but I'm not smart enough to follow.
Many types of real-world data roughly correspond to the log-normal distribution or the Pareto distribution, where simply put, there are many items with a low-to-middling value, and a few items with values orders of magnitude above the others. Examples include household incomes, populations of settlements, lengths of rivers, etc.
If you took a large-enough data set of such things, and counted each value's first digit in base-10, since lower values are more likely than higher numbers, the number 1 is very likely to be the most common number, followed by the rest in a curve that can be mathematically described.
Unless of course, either the dataset isn't one of the types of data that would conform to the aforementioned distributions (unlikely, given how historical tallies do conform), or the dataset had been artificially manipulated
I have verified it for Michigan and it appears there is voting fraud occuring. Please see:\
https://thedonald.win/p/11PpFxnhKi/verification-of-michigan-electio/