Quiet Guy
kiwifarms.net
- Joined
- Jun 6, 2020
It looks like the final tally was within about 100K: https://www.electionreturns.pa.gov/.It would take a while to go through the whole thing, and I'm trying to learn some shit I'll wind up actually use. But to look through that first argument, he basically is looking at the differences in vote values between observations for the Biden team (ie, how much it increased each time). He compiles a set of ALLLLLLLLLL the various vote increases, and then adds together random samples of all of these various increments together 10,000 different times by adding together... his 'frequency' breakdown for the number of vote distributions times ninety.
Basically, he does 10,000 runs of taking 90 different values and adding them together from Figure 2, which is measuring the number of increments of the total which fell into the ranges (0-10k, 10k-20k, etc). This completely disregards the individual vote total increments that happened - it instead rounds them, presumably up (IE a vote increase in an interval that -was- 500 is now weighted at 10k). The frequency with which he selects each of the values is based on their frequency in the total, so the 10k one is theoretically likely to be picked 60-some percent of each 'pick' in the set of 90.
Based on those 10,000 runs, he maps out the distribution of sum totals - IE, very few of the runs resulted in just 400k increases for Joe, and very many of them resulted in somewhere slightly below 900k increases. He uses this to conclude that the chance that Joe would land within 1% of Trump's total would be very, very unlikely.
Now, this is a retarded take for many reasons. First off, Joe ultimately beats Trump over this period by somewhere above 574,724 votes. I say 'somewhere above' because this paper doesn't say how much Trump's count increased over the course of it. Adding 541k (his original deficit) and 34k (his finishing margin) doesn't account for trump's own increase.
His own model suggests that Biden should have gotten somewhere under 900k additional votes from his initial deficit in Navarro's data, ~300k more than Biden actually did.
By this nimrod's metric, Biden's win would have been more "normal" if PA's voter turnout had been even higher. Assuming he'd need that extra 300k to be "normal," that means he'd need a 2% increased turnout of ALL PENNSYLVANIANS (not just VEP) in an election where PA already broke its record for voter turnout.He basically tries to conclude that by not doing as well as his model says that he should have, this is very suspicious.
Of course, as I said earlier, he would round up an interval of 100 extra votes to 10k extra votes, so that under-900k value is grossly inflated and as such the 'center' of his data is grossly inflated.
His conclusion is also flawed. It's basically "given the delta of these intervals, rounded up, and completely ignoring the fact of time (IE, late-coming batches of ballots causing very small increments and increments even of 0 getting rounded up to 10k further inflating it), it is very unlikely that he would ONLY get this much, because it is close to Trump."
But if he fucking LOST to Trump, by this model, it would be EVEN MORE UNLIKELY.
Any value that Biden landed at would in reality be very unlikely because IT'S AN ELECTION. This guy tries to fixate on the fact that he got so close to Trump, which surely must be abnormal! But then, what's normal...?
It looks like the rounding could potentially be an issue, although admittedly, I don't perfectly understand the methodology or how much the rounding could potentially affect the analysis.
In terms of the conclusion, isn't the idea that according to the data on hand it would be expected that Biden should be much more likely to win by a larger margin, and that he didn't could be a sign of irregularities, such as additional fraudulent ballots stopping once sufficient votes are acquired, or am I misunderstanding something?
Heh... I'm sure any statistician could give you an answer for that one.But then, what's normal...?