Top three graphs show Trump's numbers generally align with what is expected. Lower three show Biden's are wonky, making it more likely that the numbers are artificial instead of random.
In any type of real data the first digit follows a probability curve with "1" being the most common through "9" being the least common. In fraudulent data you can notice deviations from the expected curve of first digit frequency.
see in the image the orange curve is the expected Benford's law curve. Trump's vote numbers match pretty well. Biden's vote count doesn't fit the curve well in any of his three graphs suggesting that the numbers are made up.
This is how they caught Enron cooking the books and found out the 2009 iran elections were BS.
Look at it this way(and this is not an explanation of how it works, its just for ease of visualization): when you're dealing with something involving numbers that would occur naturally in a really large population or sample, that has a large amount of division or subcategories, you would expect that the total or value of each of these many subcategories would be similar and fairly small, adding up to the total of the population/sample with some bigger numbers that are fewer in occurrence, and bigger ones that are even fewer in number, until you get to really big numbers that rarely or never occur. If you start getting large amounts of high numbers occurring consistently, there's a chance it's fake or inaccurate because it's not consistent with the population. So, when you see trumps curve, and then another curve from the SAME population pool showing those indicators.....yeah.
It's not EVIDENCE of fraud but it's a massive red flag because its pretty consistent, and has been used for election analysis.
"In 1972, Hal Varian suggested that the law could be used to detect possible fraud in lists of socio-economic data submitted in support of public planning decisions. Based on the plausible assumption that people who fabricate figures tend to distribute their digits fairly uniformly, a simple comparison of first-digit frequency distribution from the data with the expected distribution according to Benford's law ought to show up any anomalous results."
We need this stuff being posted on r/dataisbeautiful to show the normies.
Some explanation would be nice.
What does this shit even mean?
Top three graphs show Trump's numbers generally align with what is expected. Lower three show Biden's are wonky, making it more likely that the numbers are artificial instead of random.
Benford's Law: https://mathworld.wolfram.com/BenfordsLaw.html
In any type of real data the first digit follows a probability curve with "1" being the most common through "9" being the least common. In fraudulent data you can notice deviations from the expected curve of first digit frequency.
see in the image the orange curve is the expected Benford's law curve. Trump's vote numbers match pretty well. Biden's vote count doesn't fit the curve well in any of his three graphs suggesting that the numbers are made up.
This is how they caught Enron cooking the books and found out the 2009 iran elections were BS.
INTERESTING.
This means more explanation for us, though. We're not all statisticians. But we all know the fraud is in play.
Look at it this way(and this is not an explanation of how it works, its just for ease of visualization): when you're dealing with something involving numbers that would occur naturally in a really large population or sample, that has a large amount of division or subcategories, you would expect that the total or value of each of these many subcategories would be similar and fairly small, adding up to the total of the population/sample with some bigger numbers that are fewer in occurrence, and bigger ones that are even fewer in number, until you get to really big numbers that rarely or never occur. If you start getting large amounts of high numbers occurring consistently, there's a chance it's fake or inaccurate because it's not consistent with the population. So, when you see trumps curve, and then another curve from the SAME population pool showing those indicators.....yeah.
It's not EVIDENCE of fraud but it's a massive red flag because its pretty consistent, and has been used for election analysis.
I get it now.
I relates to economic patterns, ecological patterns, sociological patterns... I get statistical anomalies.
If the data was done right, it means that non-statisticians can be more justified in their suspicions that fraud played a big role.
Also the data can be run by multiple people to confirm if it was done right. It is a falsifiable claim.
"In 1972, Hal Varian suggested that the law could be used to detect possible fraud in lists of socio-economic data submitted in support of public planning decisions. Based on the plausible assumption that people who fabricate figures tend to distribute their digits fairly uniformly, a simple comparison of first-digit frequency distribution from the data with the expected distribution according to Benford's law ought to show up any anomalous results."
https://en.wikipedia.org/wiki/Benford%27s_law
Source: https://boards.4chan.org/pol/thread/287789806
Did 4chan run this or pull it from the wild?
Not sure. But I see it as a hypothesis that is falsifiable and can be confirmed by others running the numbers.
A larger data set should align even more with Benford's law.
If they are still counting and the digits are still coming in odd, than it will continue to show misalignment.
The question is how do courts handle provable election fraud?
Maybe a re-vote can be called in certain counties/states?
Invalidate.
All provable fraud should invalidate all results, and lead to arrests on perpetrators.
Our system turns a blind eye to it.