Take the time it took to count the vote and plot that against the Republican/Democrat margin of victory (Put one on the negative axis and another on the positive). Now, what you will find is that the longer it took to count the vote, the higher the probability of a slim Democratic victory. Notice that I said a slim victory, not a democrat victory in general.
Now, if the theory is that it takes a long time to count absentee ballots and absentee ballots almost always skew Democrat, what you would expect to see is that the longer it took to count the ballots, ceteris paribus, the larger the margin of Democrat victory. But that isn't what we see. We see a correlation to victory, but we see an even larger correlation to a slim victory. You cannot use the variable to predict a Democratic landslide. You cannot use the variable to predict a modest Democrat victory. It has no relationship to the size of the victory at all apart from one and only one outcome: The photo finish, the knife-edge result. Apart from that, it tells you nothing about the margin of victory.
If the story the Democrats are telling were true, the amount of time taken to count the votes would correlate to the size of the victory. If it takes a long time to count absentee ballots, and absentee ballots skew Democrat, wouldn’t a long count tend to correlate however imperfectly with the size of a Democrat victory?
Why does it tell us about the likelihood of a slim victory but not a large one? Doesn’t this seem strange? However, this isn’t the only bit of strangeness in these recent electoral outcomes. Far from it.
Indeed, we see an unusually large number of these slim victories. And their distribution has a spike right around that 50-50 mark.
This is precisely the pattern you see when you are looking for p-hacking: You should not see a large cluster of results right around .05 that drops off suddenly; in short, the distribution of p values should be monotonic. What this means, in laymen speak, is a big spike around the statistical significance cut off point is not something you should expect to see. Valid scientific work should collectively form a distribution of p-values close to the standard uniform distribution. Something similar is at play here. The time it takes to count the votes should not suddenly drop off; it should form a smooth distribution.
More fundamentally, why do the democrats seem to win so many of these knife edge decisions? What reasonable, non-fraudulent, explanation do we have for the close calls always going one way? When referees do this, we know they are showing favoritism to the home team. If that isn’t happening here, then what is?
We can even reverse this question, what is the probability of a long, drawn out count given a small Republican lead once, let’s say, 90% of the precincts are reporting vs. what is the probability when the Democrats have a small lead. None of the explanations that depend on Democrats living in large urban areas and thus submitting blue votes in larger batches (the explanation generally given for the graph I posted at the beginning of this article) can account for this. Why is it easier to count Democrat votes in urban areas when the Republicans are way behind than when they are slightly ahead?
None of these things can be explained by absentee ballot voters skewing left, of course. However, even this often-accepted fact deserves some scrutiny. Why are we willing to accept that a major left leaning bias among absentee voters is normal?
Even if absentee ballot voters do skew left, which does seem likely, it is hard to understand why they skew this far left: To understand just how strange absentee ballot results are, you have to realize that they skew more towards the left than any other demographic factor apart from race. More than sex, more than age. Does this make sense to you? Does wanting to avoid a line tell us more about your political tendencies than almost anything else we could know about you? More than religion, sex, or age?
Now, let’s consider what has happened in past midterm elections.
Now, this year’s Senate elections were somewhat unfavorable to the Republicans because we had more Republican incumbents running and, thus, fewer opportunities to flip seats. (While incumbency is an advantage, it is an even bigger advantage to not have to run, of course.) So, I will focus on the House results.
In 1994, the Republicans took a substantial majority in the House and Senate. In 2010, the same thing happened to Obama. In 2018, the Democrats took back the House and Senate as well. The only time we have not seen an electoral backlash during the first midterms was in 2002 with the aftermath of the 9-11 attacks. With inflation at highs we have not seen in the last forty years, with supply chain issues still plaguing the economy, it is hard to believe that the Republicans could do no better than a ten seat majority in the House—a result that will appear even more anomalous if the Democrats manage to hold it, which PredictIt seems to give them about a 25% chance of doing as of the time I am writing this.
The Republicans had a larger lead on the generic ballot than they had in 1994; in fact, the lead is larger than any they have had since 1946. And Biden is polling far worse than Obama did in 2010 when the Republicans had a sweeping Congressional victory. How is it that Biden is outperforming Barach Obama?
But this isn’t everything. No, no, we have more strange results to catalogue.
In 2020, there were a number of models that had almost always predicted the winner of a presidential race: The yard sign metric, the primary test (running uncontested in your primary generally portends a win), etc. Yet in 2020 these models, which had always worked quite well all failed at once. What is more likely, that suddenly age-old techniques have become unreliable or that cheating is going on?
All of this, of course, ignores the thousands of affidavits—from members of both parties—alleging fraud in the 2020 election as well as the fact that these implausible events all started to occur right around the time a pandemic caused mail in voting to surge. Indeed, whether it be the voting machine or the loosening of the rules around mail-in-ballots, each change to the way voting is conducted brings with it a slew of suspicious outcomes.
Before you dismiss what I am saying, please ask yourself this question: If you saw this pattern in the voting data out of Chile or Russia, what would you think?
A reply to Ken White:
First thing, a lie by its nature has to be intentional. The fact that you have a category of unintentional lies shows that you are a retarded shithead who doesn't know what words mean.
Al Gore attempted to overturn a completely legal election result and nothing happened to him. Should we have attempted to lock up Gore? And, of course, there is also the problem that there was, in fact, rampant fraud in 2020. Can you explain any of the following anomalies:
1) Biden "won" the majority of military ballots. This seems unlikely given the number of Republicans among the military.
2) There were millions of ballots that only said "Biden." This seems unlikely as most voters throughout history have voted on most of the ballot.
3) Nursing homes and hospices went for Biden overwhelmingly---places where others could easily fill in the ballots for them. They went democrat at much higher rates than normal.
4) All the normal indicators (unopposed in primary, incumbent) pointed to Trump.
5) 19/20 bellwether counties were wrong for the first time in history.
6) The Cookie Poll wrong for the first time.
7) Yard sign predictors were wrong.
8) Rally turnout prediction wrong,
9) Mail in ballot rejection rates go from about 3% to .003%
10) Poll watchers sent away in key locales, counts stopping nearly simultaneously, then counting resuming after the poll watchers are gone.
Vladimir Putin rigs his elections better than you dirty Democrats did. But, of course, the obviousness of the rigging was part of the strategy: There is nothing like some good gaslighting to finish off a psychologically vulnerable opponent.
https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA18583