Benford`s Law Election Results 2020 Reddit - Quant Dynamics
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“ fact is that there is no corresponding behavioral or theoretical reason to believe that the [rank] numbers of the non-fraud data will be different from the data of an election imbued with cases of falsified votes. There is [also] no reason to believe that the fraud-free data itself will comply with [Bendford`s law]. Nor does it tell us when fraud could reconcile official data in accordance with Benford`s law. In fact, using both real and simulated data, this is exactly what can happen. However, Biden`s vote count is the only one that doesn`t comply with Benford`s law in key states. Guess which ones……… Yes, those who were returned the day after election day. Simulations with fair and fraudulent election data show that Benford`s law is no better than the push of a button to identify fraud. However, this observation does not apply to all stochastic processes; In particular, it does not apply to electoral data. After reading this article, I looked at the comments and there were people who claimed they had left and checked themselves just to find the same results that the article claims. At the end of the article, there is a link to KDnuggets DOT com, where they will teach you how to apply Benford`s law so that you can also check the election results. Nevertheless, the Mebane article also stated in the discussion: “In any case, the 2BL test alone should not be taken as evidence that electoral fraud took place or that an election was clean.

A significant result of the 2BL test can be caused by complications other than fraud. Some types of scams that the 2BL test cannot detect. Since Donald Trump claimed electoral fraud after/during the 2020 election, some have cited Benford`s law as evidence of fraud. Benford`s law suggests that there is an expected top-down distribution of the first number of each sample – in this case, the total number of county votes. For example, if the total votes for County A were 21,342, then “2” would be counted. The count totals of these counts are summed for each state. With Pandas and Jupyter, I calculate the expected amount against the actual value of all states in 2016. The results suggest that this is NOT a useful indicator of anomalies for 2020 (or any other choice) as the average error rate is around 78%. social media users shared posts stating that a mathematical rule called Benford`s law provides clear evidence of fraud in the U.S. presidential election. However, research and academics seen by Reuters agree that a waiver of Benford`s law does not prove that electoral fraud took place.

It`s funny, because there`s a lot of real data on both types of elections. There`s no need to simulate anything to prove your point – unless, of course, you didn`t get the desired result from using real data. I replied to someone else below – The summary claiming that it does not provide a correct analysis of electoral fraud was actually not serious because they were not applying the law correctly. “It is common knowledge that the first digits of the district vote count are not useful for diagnosing electoral fraud,” he wrote. Edit2 (or similar: eight. But who counts? Certainly not Nevada): Well, it`s now through most of the comments voted, and I`m wrong. 2020 runs until kick-off. IMO, what determines the sketch is the verification of postal votes against the list of the deceased. In the past, when electoral fraud was known (for example, Kennedy`s election), a group of deceased people voted and influenced the outcome. Checking all postal votes against the list of the deceased takes forever, so it`s never done. Even during Kennedy`s election, rumors circulated at the time, but they were never proven. One thing I`ll note that others probably already know: tracking Benford means the data fits a world without voter fraud.

It alone does not exclude any fraud. The title in DataIsBeautiful could be read to indicate that this is the case. Edit: goddammit, I joined the propagators of electoral disinformation. u/spirit-bear1 has the correct explanation of Benford`s law below. I support “people are bad at random numbers,” although elsewhere a study called “Benford`s Law and the Detection of Election Fraud,” published in 2011 by Joseph Deckert, Mikhail Myagkov, professor of political science at the University of Oregon (here), and Peter Ordeshook, professor of political science at Caltech (here), found that Benford`s law was “problematic at best.” if it was applied to the elections: “We see that the agreement and the deviations from Benford`s law do not follow any model. […] Its “success rate” is essentially equivalent to a tospoke of a coin, making it problematic at best as a forensic tool and completely misleading at worst. (here) By way of preface, all data on election results from one county to another is still referred to as “unofficial,” including the Milwaukee votes currently circulating, which are used as an example of voter fraud. That`s what brought me here! The article in question can be found on gnews DOT org SLASH 534248 SLASH. WARNING!!! DO NOT share the article on Twitter, they currently automatically block anyone who does. After reading the article, I don`t know why they would ban people because of this, it`s ONLY the application of Benford`s law to the election. But the downside is that Benford`s law doesn`t necessarily apply to politics.

Districts vibrate either democratic or republican. There is nothing “random” about geography. If you live in a Democratic district, you are expected to vote for the Democrats. It`s not really random. Where it is more difficult to draw conclusions is in the swing districts. These people tend to change their vote from election to election, so it`s harder to figure out what to expect. When we look at simulations designed to model both fair and fraudulent competitions, as well as election data that we know has been impregnated with fraud based on other research or is unlikely to have experienced measurable wrongdoing, we find that compliance with and deviations from Benford`s law does not follow any trend. It`s not just that the law sometimes deems an election fraudulent as fair or a fair election as fraudulent. Either way, its “success rate” is essentially synonymous with a coin tospoke, making it problematic at best as a forensic tool and completely misleading at worst. The specific case of the Milwaukee results was also studied by Professor Boud Roukema of the Polish Nicolaus Copernicus University. Roukema considered applying the Benford Act to Iran`s 2009 elections (

He told Reuters by email: “A big mistake in applying Benford`s law to milwaukee`s results is that the logarithmic distribution — how many `powers of ten` there are — is very narrow in the number of votes per district in Milwaukee. In other words, half of all districts have a total of votes of about 570 to 1200, and the logarithmic (average) average is about 800.

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