Time series analysis of mail-in ballots stats reveals widespread fraud | Hacker News Reader

Time series analysis of mail-in ballots stats reveals widespread fraud

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10 pointsbra-ket15 comments

This is quite a political post, but I want to focus on a pet peeve of mine

The phrase "the exception that proves the rule" is meant to say one of:

1) this exception shows that the rule is needed, as in "the exception that proves the need of a rule"

2) if a case is extremely execptional then there must be a normality which is the rule, as in "the exception that proves the existence of the rule"

There is no authority argument here, I simply like it best this way.

After some thinking I realized that there is indeed a continuum between "the exception that prove the existence of a rule" and "the exception that proves the correctness/validity of a rule" and in this post it is unclear which meaning was intended.

Anyway I believe my point still applies to most common usages.

Why is this flagged? It provides cold hard data and a clear explanation of the data.

See https://twitter.com/APhilosophae/status/1325592112428163072 for the Twitter feed.

Or https://threadreaderapp.com/thread/1325592112428163072.html

For the data: http://s000.tinyupload.com/?file_id=14566999163598825215

At https://news.ycombinator.com/item?id=25032319 I point out that the model used is overly simplistic.

I'll be stronger. The analysis is made of cherry-picking and slanted opinion to make you think there's a controversy when there isn't.

It starts with the implication that the author is revealing something secret that's going to be covered up, which is a great way to lure in the gullible.

But ignore my commentary. Consider:

> Around 3am Wisconsin time, a fresh batch of 169k new absentee ballots arrived. They were supposed to stop accepting new ballots, but eh, whatever I guess.

> By 4am the D to R ratio was all thrown out of whack. That is because these ballots were not sampled from the real Wisconsin voter population, and they were not randomized in the mail sorting system with the other ballots. They inherently have a different D to R signature...

> than the rest of the ballots quite possibly bc additional ballots were added to the batch, either through backdating or ballot manufacturing or software tampering.

Yet it's very easy to find that those 169K ballots came from Milwaukee. According to the presented model, rural areas tend to be more R and counted later, but here's a non-rural area coming in late, and from a place with more Ds. Hence the model is clearly broken and certainly doesn't have the power to say "quite possibly". The model ignores how the Republican-controlled legislature in Wisconsin refused to allow early counting, so many ballots would not be counted until after Nov. 3, rural or otherwise.

The author asserts that "These outlying areas take longer to ship their ballots to the polling centers" but there's no clear explanation why that's the only possible factor to consider.

The author repeats the assertion for Pennsylvania:

> But then as counting continues, the D to R ratio in mail-in ballots inexplicably begin "increasing". Again, this should not happen, and it is observed almost nowhere else in the country, because all of the ballots are randomly shuffled...

Except that there too the Republican-controlled legislature refused to allow early counting.

Ditto for Michigan ... which is another state the author singled out with "both signs of contaminated ballot dumping, and ballot ratios drifting toward dems when they should not be".

So three states where the absentee ballot counting couldn't start until voting, which are the same states the author suggests has fraudulent voting practices because of deviations from the author's model. Now, why we should we assume the model works for those states when the counting procedure is so different?

If we trained our model on PA, MI, and WI could we not assume there was fraudulent voting in the other states?

And why do we assume that "all of the ballots are randomly shuffled"? I sent my absentee ballot to the county. I assume PA is the same. If it takes longer to count the ballots in populated areas than rural ones, then we would expect the votes from Philadelphia county to come later than those from rural counties.

So no, I don't think it's a clear explanation, because it there's no explanation of why the many possible confounding explanations which seem more likely than massive election fraud could be ignored.

they don't like anything partisan here, but especially if it favors the right.

I can guarantee you, the same post, with the same messy data and analysis, but on 2016 elections (in favor of Hillary) would have been on the front page for entire day.

this is some anon in 4chan , scraped from a NYT website allegedly . since we have only a single source of truth and nothing to have as a reference why should anon be trusted ..... besides DJT would have this investigated with his DOJ if something is off by this much, this would also mean the FBI / NSA / CIA was sleep at the wheel ... unless you are able to trace all of the ballots this is just pure fiction

DJT is preparing lawsuits with evidence of fraud, ballots and counting manipulation. Most swing states (where Biden leads with thin margin) are still counting the provisionary ballots, recounts and audits are pending.

Biden was proclaimed a winner prematurely by media, Americans deserve a fair election.

as DJT demonstrated so gracefully with the whole russia there is a gulf of difference between proof and conviction ... Also can we all just level with the fact that both dems and repubs are sore losers .

Better readability and links to data set here: https://threadreaderapp.com/thread/1325592112428163072.html

to give a bit more insight; it says essentially, imagine a deck of cards at a casino, shuffled by machine 50, 100, 500 times. That's what the ballots are like shuffled through the USPS....homogeneous....there's no cream rising to the top of the bottle, and there's no stacks of Kings or Aces lodged all in one spot because the system (mail) shuffled them.

There should be no discernible patterns of any kind.

One for you, two for me, two for me, two more for me, three for you, one for you, one for me, two for you and so on, all leading on his graphs, towards a very straight line.

So there shouldn't be a stack of ballots that suddenly burst on to his graphs leaning heavily to one group or the other at any given time....but there is....in multiple states, all happening in the wee hours of the morning.....and all happening on the 4th.

And in each state, they all (on the 4th), lean Democrat.

Discernibly. And with one sole exception, only in those states.

or let's say there's a batch of 100k votes and it's split 90k Biden votes and 10k Trump votes (in Democrat leaning precincts). In any random sample there should be a 9:1 ratio meaning that out of 100 randomly selected votes ~90 will be for Biden and ~10 will be for Trump.

The larger the sample the more likely the actual count is closer to the 9:1 ratio. Any deviation from that ratio will be made up as the count goes on. So in the most extreme case, if 90k in a row were all Biden and there was a 90k spike, the next 10k should spike for Trump. That never happened because after the 4am Biden spike, the count went back to "normal" ratios.

To use the casino analogy imagine if you're at the roulette table and it comes up red 100,000 times in a row without black or green ever being called. That is statistically improbable.

The only other explanation would be that NYT dataset is messed up.

Only an audit can prove this for the court.

Let's hope they audit swing states results and do similar analysis/anomaly detection.

> There should be no discernible patterns of any kind.

That's not a correct description of the model given.

The author writes "The slight drift from D to R mail-ins occurs again and again, and is likely due to outlying rural areas having more R votes. These outlying areas take longer to ship their ballots to the polling centers."

That is, the author says there should be a discernible pattern.

Note that this is a hypothesis which was created only after looking at the patterns in different states. While it appears to be true of some other states, there's no strong argument for why it should be true of all states and all vote counting methods.

Unfortunately, the author then double-downs by saying that since this is the expected pattern, and it wasn't seen in PA, then something's suspicious, to the point of being "likely evidence of ballot backdating or manufacturing."

The IMO correct interpretation is observation that the random model, which was already tweaked in order to explain one data trend, might need more adjustments in order to explain other data trends.

For example, the author uses PA and WI as examples of outliers to the drift-to-R-mode. But we know that (quoting https://eu.usatoday.com/story/news/politics/2020/11/06/how-s... ):

> While most states began processing mail-in ballots before Election Day, others had laws preventing election officials from doing so. For instance, election officials in the swing states of Wisconsin and Pennsylvania requested the ability to begin processing earlier and the Republican-controlled legislatures in those states refused, all but ensuring the high volume of mail-in ballots would not be counted until after Nov. 3.

There's no reason to expect that a model trained on states with one vote counting model should be applicable to states with another vote counting model, much less be strong enough as to justify a claim of ballot backdating or manufacturing.

Furthermore, I believe there's a reasonable argument that the "Republican-controlled legislatures in those states refused" in part to muddy the waters, and create anomalies which could be interpreted as fraudulent voting.

This potential confounding issue was also not included in the "deck of cards at a casino" model.

(actual question as you seem well informed)

What about the slow D trend for mail ins? could that be that the same 4 Nov spike got spreaded over more days? Because one would expect that the very last to arrive ballots should trend R the same as rural areas

I am not well informed.

I just know that "assume a spherical cow" analyzes - ones which assume everything is uniform - are suspect from the get-go. My suspicion was further supported when the model failed to include well-described factors which I think would reasonable cast doubt on such a simple model. And the commentary failed to discuss why those factors were rejected, preferring instead to jump right to a claim of fraudulent voting, which lead me to conclude the author was even less informed than I.

A recurrent problem in data^Wscience is that it's easy to find false signals when trawling through data. As Feynman commented: "The first principle is that you must not fool yourself and you are the easiest person to fool."

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