itfitzme
Silver Member
The mail in ballots were delivered to Joe Biden after the vote counting was in large part over. Hundreds of thousands of COA and deceased people were on the rolls, these were probably used if the voters were not made up. This seems to have happened in many states.
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Here is what we see. First the in person voters vote arrive - depending on where they vote the areas can break heavily for Trump or Biden so we see a large scatter. Afterwards the mail in ballots arrive, which are mixed and thus have a roughly constant ratio of Biden and Trump voters. After the midnight however, the mail in ballots start favoring Biden, which indicated votes have been added from other sources.
In other states we see the opposite pattern, the mail in votes ratio shifts towards republican presumably because of the different transit from the rural republican areas.
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"It Defies Logic": Scientist Finds Telltale Signs Of Election Fraud After Analyzing Mail-In Ballot Data | ZeroHedge
ZeroHedge - On a long enough timeline, the survival rate for everyone drops to zerowww.zerohedge.com
And this is also what the new lawsuit in Michigan argues. We will see more lawsuits in other swing states I am sure.
FALSE.
1. "presumably" In other words, your guess. "which indicated" ... again, so you guess. That's not how statistical inference works. You are eyeballing a scatterplot and guess about probabilities without any statistical analysis at all. What is your test? Null hypothesis? What is your confidence interval? Alpha? p-value? Anything? No? Then you are just making things up.
2. High population density counties favors the Democratic candidate. High population density counties also have a hell of a lot of votes to count. Therefore, later batches will be from counties favoring Democrats.
3. Oh, here's another reason why Biden got more votes.... Because more people voted for him. How does Occam's Razor go? The simplest explanation is the most likely one.
You just flunked Statistical Inference 101.
Your analysis is a perfect example of Lies, Damn Lies, and Lies with made up statistics.