Dominion Election Machine Results 2022

Prior to the election, I announced a test of whether Dominion machines correlate to the 2022 results. This test consisted of taking the Real Clear Average polls prior the election and comparing them to actual results, using the percentage of Dominion machine usage in a given state as a predictor.

The 2022 results were the Dominion effect was 4.06% with a one-sided p-value just under 10%


Ten days ago I wrote what type of statistical tests I would do regarding Dominion Election Machines.

I warned that all eyes were on the Arizona governor’s race, where the Republican was ahead (slightly), the Democrat candidate was in charge of her own certification process, and Maricopa County uses Dominion machines–which would have been targeted for cancellation if the Republican won.

Unfortunately, the machine processes failed in some areas, at some points in time, in Maricopa County. I do not know which type of machines (printer vs. tabulator) or which brand of machines caused this issue. I read various “fact checks” that try to blame the printing companies not the tabulators, but the fact of the matter is, the official statement came from video from Maricopa County itself via twitter, which said “20% of the locations have an issue with the tabulators”. Watch the video for yourself, if it is still available. (Here is an article about the person on the right in the video.) I have no knowledge of what machines caused this issue.

So…. where does that leave us?

Unfortunately, it means that a large number of individuals in our society will not trust the outcome of this Arizona election, independently of any statistics I am about to produce.

Having said that, here are some stats to consider.


Table 1 shows the number of elections (for governor or for senate) that were included in the study. Note that the Oregon governor polls were a tie going into the election, so I excluded Oregon from this table to make it easier to read.

Table 1: Overall View
CategoryTotalRepublicans Predicted to Win Republicans Actually Win Democrats Predicted to WinDemocrats Actually Win Change
Dominion Less than 15% of Potential Voters1066440
Dominion Higher than 15% of Potential Voters13627114

Table 2 shows you the data that is used in the statistical analysis. The first two columns with numbers are the value that Democrats were higher (or lower) than the Republicans. The difference is the change from the predicted to actual.

Table 2: Data for Statistical Test
StateElectionRCP Avg 11-5-22 Dem. AdvantageRCP Actual 11-16-22Difference (Y Variable)Dominion Percent (X Variable)
New YorkGovernor6.25.7-0.555%
New MexicoGovernor46.42.4100%
New HampshireSenate0.79.18.40%
North CarolinaSenate-5.2-3.51.70%


Table 3 shows you the output of a statistical regression.

The first field called “intercept” is how far off the polls were from the actual results. The value is 0.66, meaning the general population appeared to vote 0.66% more for Democrats than the pre-election polls. The P-value is 68% which is generally considered not significant. This means the polls were not far off from the election results.

The next field is the number of points the model estimates were moved based on how much of the state was covered by Dominion machines. If the state is 100% covered by Dominion, the expected change from pre-election poll to actual results is 4.06%. For example, if the pre-election polls were a tie, 50/50, going into the election, this model predicts a fully Dominion state would be 52.03/47.97.

A generic least squares model would do a two-tailed t-test, which would produce a p-value of 20%. However, when I announced the test 10 days ago, I pre-announced that we were testing for a positive coefficient. This means that the correct test is a one-sided test. The p-value using this test is 9.99% which is approximately 10%. This means that, given the test I announced prior to the election, the probability that we would obtain a coefficient of 4.06 or higher is about 10%. In other words, this test had a 1 in 10 chance of achieving a Dominion coefficient this high or higher.

Table 3: Results
Intercept (How far off were polls, after adjusting for Dominion Effect):0.6668%
Dominion Coefficient (Percentage point change based on what proportion of voters are covered by Dominion)4.069.99%


Here is the spreadsheet for you to see the math.

What does this mean?

It could mean nothing. Statistics are random.

However, the article you are currently reading and my article from 2020 show:

  • There are correlations from 2022 which correspond to people’s suspicions of fraud.
  • There are correlations from 2020 which correspond to people’s suspicions of fraud.

Producing correlations two elections in a row should make the general public understand that there may be something going wrong, and it continues to be appropriate to push for transparency.

At the moment about 100,000,000 Americans do not trust the results of the 2020 election, as polls show 1/3 of the more than 300,000,000 Americans do not trust the results.

For our society to stop having 100,000,000 people who do not feel like their election choices are being fairly counted, it is an imperative that society do one of the three:

  • Develop a process to make the overwhelming majority feel that machine voting is fair; this is urgently needed for Dominion, for example;


  • Stop using machine voting;


  • Stop using specific machine brands that have not developed a process for people to verify and feel comfortable that the machines are fair.

Stats for Nerds

Okay, above I showed the outcome of a pre-announced test.

If you are interested, here are a few other things to consider, some strengthen the theory and some weaken it.

Question 1: From a data perspective, what really happened?

Answer: Florida, Oklahoma, and Texas, which are not heavy Dominion states broke hard for Republicans, while most of the other places broke Democrat. Some states that were not heavily Dominion broke hard for Democrats, such as New Hampshire and Washington. If the theory about Dominion machines were true, we would not have expected the New Hampshire and Washington to break this way. The below chart illustrates the results and the modeled output, using the model above. Notice the wide range of values where Dominion % is close to 0. This is not a sign of a particularly good model.

Question 2: Did the polls miss how the Hispanic vote would break?

Some people theorize that the polls underestimated that the Hispanic vote would swing Republican, which is why Florida, Texas, and to some extent New York didn’t follow the rest of the nation which went more Democrat than some anticipated.

Answer: If you add Hispanic vote to the regression, it does show Hispanic vote swinging towards Republicans, and the Dominion effect strengthening to 7.54, with a one-sided t-test with a p-value of 1.7%. If this theory were true (that Hispanics broke for Republicans but Dominion broke for Democrats), it would be a reason why Nevada, Arizona, and New Mexico still ended up mostly Democrat while Florida and Texas went hard for Republicans.

However, I am not in favor of adding variables to a model of this type with so few observations.

Attached is a spreadsheet a friend of mine made.

Question 3: Did the Real Clear Politics polls change after you took the data over the weekend prior to the election?

Yes. And I went back and looked at them after the election, and I did not feel I could use what the website showed. In some states it stopped showing the “RCP Average” and just showed individual polls.

Question 4: Shouldn’t the real test be the actual outcome of the election, meaning win or lose, not whether Florida runs up the scoreboard?

Answer: I think that is a good way to look at how this should be tested. To do this, I ran a test on the outcomes, win or lose, and used a logit regression. There was a warning generated which indicates it may not be a good fit. The results were that the Dominion effect was 3.44 with a two-sided p-value of 32%, which I think would correspond to a one-sided p-value of 16%. Again, there were warnings, which indicates that there was not enough data points for this type of regression to be a good fit. Attached is the data and the R code:

Question 5: Were there any races that went against the Dominion theory?

Answer: Yes. Nevada is a heavy Dominion state, yet the governor went to the Republican. The Repulican in Wisconsin winning in a close race for senate is also interesting.

Question 6: What do you really think about this overall test?

Answer: It was interesting to make a hypothesis prior to the election and test it. However, I don’t think there really are enough data points in this test to get a good fit. I think the work I did in 2020 was much more comprehensive because it had much more data. I do note that both tests showed a correlation. Two tests in a row should strengthen the need for transparency. When I first heard the theory about Dominion, it sounded crazy to me, but the correlations are there.

Dominion Election Machine Hypothesis Test, Setup Prior to 2022 Election

As many know, I wrote a paper about the 2020 election which tested whether there was a correlation between Dominion Voting Machines and the 2020 presidential election. (This paper can be seen here:

The paper concluded that there was a correlation between counties that had implemented Dominion and the change in election outcomes comparing 2008 vs. 2020. I made no attempt to assess the cause of this statistical correlation. The test outcome could have occurred randomly. If it was not random, the reasons behind the correlation could have been unrelated to the voting machines, or if they were related to the voting machines it could have been related to some hacking technique unknown to election and Dominion company officials.

At the conclusion of the paper I called for audits (not recounts) to assess what happened. The only place such an audit occurred was in Maricopa County, Arizona.

As we approach the 2022 election, a common question asked of me is what I would expect to happen in 2022 if there is actually a persisent correlation between Dominion and positive results for Democratic candidates.

From 10,000 ft. up, I would expect if the Dominion machines are somehow influencing elections that the Republican candidate for governor in Arizona, Kari Lake, to lose. Right now Kari Lake is winning in the Real Clear Politics average of polls. One of her main campaign issues is she wants to change election processes. It seems apparent that Lake would do everything in her power to remove Dominion from Arizona, so it conversely seems that if there is some control of the election emanating from Dominion machines, that those forces would defeat Kari Lake.

Compounding the Arizona situation, Lake’s direct opponent, Katie Hobbs, weirdly and unethically, as acting secretary of state, is overseeing her own election.

If Lake wins, this would be evidence that Dominion machines are not controlling elections. Even after winning, I suspect Lake will remove Dominion machines from Arizona. 

But if Lake loses, I expect the people of Arizona to be quite upset. And, if that were to happen, simply saying “Kari Lake lost, so there must be fraud” will not be enough. There needs to be more evidence that something went wrong. So, I have thought about this in advance and I wish to share what I plan to study after the elections close on Tuesday (I am typing this on Sunday 11/6).

I wish to test the difference between Real Clear Politics averages versus final results. There should be a national swing that happens, either for Democrats or for Republicans, but this national swing should be independent of which machines a state uses.

I took Real Clear Politics Averages over the weekend for the governor and the senate and they look like this:

State Election RCP Avg 11-5-22 Dem. Advantage
Maine Governor 6
Oklahoma Governor -1
Oregon Governor 0
Texas Governor -9.2
Kansas Governor 3
Florida Governor -11.5
Minnesota Governor 4.3
Pennsylvania Governor 10.7
Wisconsin Governor -0.2
Michigan Governor 4.4
New York Governor 6.2
Arizona Governor -1.8
Colorado Governor 11.7
Nevada Governor -1.8
New Mexico Governor 4
Connecticut Senate 11
New Hampshire Senate 0.7
North Carolina Senate -5.2
Washington Senate 3
Florida Senate -7.5
Ohio Senate -6.7
Pennsylvania Senate -0.1
Wisconsin Senate -2.8
Arizona Senate 1
Colorado Senate 5.3
Nevada Senate -2.4

For these locations, I know the percentage of the state that appears to use Dominion machines.

State Election RCP Avg 11-5-22 Dem. Advantage % Dominion
Maine Governor 6 0
Oklahoma Governor -1 0
Oregon Governor 0 0
Texas Governor -9.2 0
Kansas Governor 3 5
Florida Governor -11.5 8
Minnesota Governor 4.3 14
Pennsylvania Governor 10.7 19
Wisconsin Governor -0.2 31
Michigan Governor 4.4 54
New York Governor 6.2 55
Arizona Governor -1.8 60
Colorado Governor 11.7 92
Nevada Governor -1.8 98
New Mexico Governor 4 100
Connecticut Senate 11 0
New Hampshire Senate 0.7 0
North Carolina Senate -5.2 0
Washington Senate 3 1
Florida Senate -7.5 8
Ohio Senate -6.7 13
Pennsylvania Senate -0.1 19
Wisconsin Senate -2.8 31
Arizona Senate 1 60
Colorado Senate 5.3 92
Nevada Senate -2.4 98

I intend to complete the analysis after the election. What I mean by that is I will take the difference between the predicted vs the actual and run a regression that includes the Dominion percentage as a potential influencer. If a positive coefficient for Dominion emerges that is statistically significant, this would be further evidence of a connection between the machines and election outcomes.

Of course there is always true randomness. This means that whatever such a model produces only provides evidence but does not provide conclusive proof. Random events are just that, random.

I have attached the spreadsheet I intend to use for this test. By publishing this spreadsheet prior to the election I hope to avoid accusations of post hoc rationalization.

Right now this spreadsheet allows the user to simulate the election. However, after the election, the simulated election values will be replaced with actual values and the statistical test result will be produced. Please note, I have excluded Georgia, because of a complicated relationship with their incombent governor and Dominion machines, and Kansas and Maine because the polling data is too small. All other races were included that were available from Real Clear Politics when I wrote this.

I’d like to make one other point. I really hope the election results are such that our society can be comfortable with the outcome and we can move past the idea that the machines are somehow influencing results.

Florida will likely keep PIP for at least the next 18 months. Here is what to do.

Florida will likely keep PIP for at least the next 18 because Ron DeSantis vetoed the bill that would have repealed PIP.

This means that the house and senate would need to override his veto for this bill to go in effect. Because the Florida legislature is out of session, and is unlikely to have a special session because of this particular veto, the legislature cannot address PIP concerns until next winter, and unlikely will they make any law that does not give insurance companies time to prepare. So, I believe the earliest we could see any type of change to auto insurance law is December 31, 2022.

This means that FL PIP, in its current form, will be in effect for at least the next 18 months.

What should you do?

Contact Your Legislative Representatives

If you are a citizen of Florida, I urge you you to contact your legislative representatives and demand attorney fee reforms with regards to PIP. Explain to the representatives that the biggest problem with auto insurance is that a claimant can assign benefits to a third party (such as a medical clinic) and that this clinic can sue an insurance company and collect one-way attorney fees. For example, Person A is in a car crash. Person A can go to Medical Clinic B. Person A signs a form allowing assignment of benefits to Medical Clinic B. Medical Clinic B then assigns the case to Lawyer C. Lawyer C takes all PIP claims from Clinic B and files individual lawsuits on EACH claim. It costs the lawyer very little to do this; he/she just uses a template for each claim, only changing a few details for each lawsuits. However, if Lawyer C wins any of the claims, he/she can claim attorney fees for the claim. If your legislative representatives want to understand further, point out to them that the PIP statute itself contemplates assignment of benefits, as seen at Florida section 627.736 (10)-b-1, and that once assigned, if the lawyer (suing on behalf of the medical clinic) wins, he/she receives fees per 627.428, but if the insurer wins, it receives nothing and still bears the cost of defense.

Explain that the easiest way to bring premiums down for auto insurance is for 627.428 to be amended to say “this section does not apply to attorney fees where the attorney represents a third party who received the claim via an assignment of benefits.” Not only would this lower auto premiums but, such a statute would dramatically improve the homeowners situation as well. However, if they want to limit it just to automobile claims they could pass “this section does not apply to claims brought under PIP coverage when benefits have been assigned to third party vendors, such as medical clinics, supplying services to the claimant.”

Explain to your representative that Florida had 400k of these type of lawsuits in 2020 and will likely have more than 500k in 2021.

If You Work for an Insurance Carrier, Consider These Steps

a) Review your incoming claims for patterns of abusive lawsuit behavior. We have a free tool available for this. It can rate an incoming claim for this very behavior based on the NPI of the PIP bill. We will gladly give you a username for free, and there is no expiration date or hidden fees. Determine if there are clinics that are abusing the process to make an unusual number of claims against your carrier, and whether these clinics using the threat of lawsuits to coerce you into paying questionable claims.

b) For clinics who appear to be abusing the process, consider other information you have about the incoming claim and evaluate whether 627.736 (6)-g applies. This statute says:

An insured seeking benefits under ss. 627.730–627.7405, including an omnibus insured, must comply with the terms of the policy, which include, but are not limited to, submitting to an examination under oath. The scope of questioning during the examination under oath is limited to relevant information or information that could reasonably be expected to lead to relevant information. Compliance with this paragraph is a condition precedent to receiving benefits. An insurer that, as a general business practice as determined by the office, requests an examination under oath of an insured or an omnibus insured without a reasonable basis is subject to s. 626.9541.

Based on the fact pattern of the individual claim, coupled with the activity of the clinic, it may be prudent for you to withhold payments until you are able to have an examination under oath with the claimant. I have found that in some cases, the claimant will actually disavow the billed medical services saying that he/she was unaware that the clinic billed for all of that activity. This allows you to withhold payments and also helps you build a criminal case against the clinic.

We have an affordable commercial tool, for which we offer free usage for evaluation (this is a beefed up version of the free tool discussed above) which can help you determine if a claim is a good candidate for an examination under oath. I have seen excellent results from this process.

Closing Thoughts

FL PIP is currently rewarding trial lawyers and fraudsters. I understand why the legislature wanted to get rid of PIP. I also understand and agree with the governor’s assessment that the new bill would have been worse than the current law. For Florida’s rates to drop, either 1) the law needs to stop rewarding lawyers via one-way attorney fees and/or 2) insurance carriers need to improve their methods for preventing fraudulent payments.

Unless carriers are actually willing to withhold payments while they conduct examinations under oath, rates will not come down; for every fraudulent clinic owner that is put in jail, another one appears and fraudulent claims continue to be paid.