We will be publishing a series of articles over the coming days which will explain how to identify fraud at any election, by looking at and analyzing the data for yourself. This follows on from the trend analysis and heat maps done by Seth Keshel, which we cover extensively on this site.
Some parts get a bit technical, but we’ll do our best to break it down in simple terms with clear examples.
By the time you finish reading this series, you the reader will have the necessary knowledge to apply these simple techniques to discover anomalies and fraud on your own.
You will also come to the realization that all the proof you need to ascertain the legitimacy of the 2020 election results is already in the data.
Find out how and why in the following articles.
The First Rule of Data Analysis
The first and most important concept of any data analysis is understanding the importance of trends and knowing how to find them.
Remember: The trend is your friend!
As you follow the next few pages, keep an eye out for statistics that continually move in a similar direction or pattern, and any others that break or “buck” this pattern.
When it comes to analyzing election data, there are several different “trend” scenarios to consider:
- Historical General Election trends within a county
- Historical “Down Ballot” race trends within a county
- Historical registration trends within a county
- County trends within a state
- “Votes / Registrations” ratio trends by age within a county
- Historical “Voting Rates” for each state
The questions you need to be constantly asking yourself are:
- Is there a trend hidden in the data? (Can you identify one?)
- Does the trend make sense?
- Does it manifest itself in other data sets?
- Is the shape of the trend “flat”, “increasing” or “decreasing”?
- Can we find correlations between different trends?
- Can we find a good, rational explanation for a broken trend (i.e. a change in direction)?
- Can the trends be categorized across time, counties and states?
Before we dive into the details it is important to get an understanding of the (simpler) “high level” trends first.
It is always best to first grasp the overall dynamics of the system under analysis, before diving into the details. And even when you are deep in an analysis, always keep one eye on the big picture.
Articles
The simplest (yet possibly the most important) trends to start with are covered in our first three articles:
- The Fall of the Bellwether Counties
- The Battle of the Largest Counties
- The Curious Case of the 2020 “Voting Rate” Blowouts
We recommend reading each of these and completing the associated tasks. The tasks will help you attain a deeper affinity regarding the importance of trends within data analysis. These overview articles will make the subsequent detailed trend analysis easier to comprehend.
Articles In This Series
| Overview | Diving deeper into the unusual trends and statistics discovered in the 2020 election. |
| Part 1 | A look at the surprising failure of the bellwether counties in 2020, and what that tells us about the Presidential election outcome. |
| Part 2 | The data shows the Democrats are winning less and less counties at each election, but are winning more and more of the largest counties. How is this possible? |
| Part 3 | Voter turnout rates shot up dramatically in many states in 2020. We look at the voting rates since 2000 to find out which states set a new record. |
| Part 4 | Learn how to "normalize" a county's winning margin to identify abnormalities. What does this reveal about the 2020 election? |
| Part 5 | Learn how to use the party registration numbers for each county to predict the election results for a state and assess the likely validity of the results. This is a guide on the method Seth Keshel uses for his predictions and county heat maps, allowing you to dig into the trends of your own county and uncover anomalies. |
| Part 6 | We build on some of the previous techniques to scan 3,111 American counties, identifying those whose shift in vote totals moves unexpectedly against the shift in party registrations. |
| Part 7 | We compare the Democrat vote totals with previous elections which reveal some very large increases in unlikely places. |
| Part 8 | We compare key parameters in the 2020 results against the previous five elections using the z-score, and find hundreds of counties breaking statistical norms. |
| Part 9 | Interesting statistical findings on how the Presidential race results compared to the other races in the same election. |
| Part 10 |
Voter Roll Analysis
Coming soon.
|
| Part 11 |
The Art of the Steal
Coming soon.
|
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Conclusion
We have looked at several different trend analysis techniques to help people analyse the data from the 2020 General Election for themselves.
Each of these techniques is sufficient, on its own, to raise serious doubts, enough to warrant further investigations into the 2020 General Election results.
We believe that when combined, these techniques form a “Nomological network”, and provide a convergence of evidence, that make the proof of fraud irrefutable.
The The Fall of the Bellwether Counties article should be proof-enough. We hope you can understand the sheer unlikelihood of 21 bellwether counties getting it wrong out of 22. (Not to mention that Clallam County’s results look dubious enough that it’s possible that none of the bellwether counties truly voted for Biden.)
Could it be, the bellwether counties actually got it right and that something else went horribly wrong?
We hope you’ll absorb this information and start demanding satisfying answers to these questions.
⚠️ Would you ignore a canary warning in a coal mine?
Ignore the bellwethers at your own risk.
Further Reading:
If you find these articles meaningful and convincing, please share them with friends and relatives. The more people see for themselves that something is wrong, the more we will be able to hold elected officials accountable.
Feel free to forward a link to your elected officials as well.
Inquiries
If you need assistance, clarification, or would like to report an error, please read our Election Data Inquiries page first. Then create an issue on our GitLab forum to initiate a conversation.
Visitor Comments
Did you find these articles helpful? Did you dig in and review the data for your county or state? What did you find? Let us know in the comments below.