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I'm Dave Lobue and I was the last testimony at the Georgia Senate Hearing talking about our data evidence of vote drops. Ask Me Anything
posted ago by Brick ago by Brick +6178 / -2

Hi TDW!

I'm Dave Lobue, and you might have watched our testimony from DIG (Data Integrity Group) at the Georgia Senate hearing yesterday: https://rumble.com/vcay7j-data-scientists-shocking-election-testimony.html

Here's where you can find a list of our videos:

https://rumble.com/user/ElectionNightFacts

I've got 12 years experience in data science and analytics. I have a BA in Philosophy, Masters in Business/Marketing from Grenoble Ecole de Management in France, and currently I'm pursuing a Masters in Data Science from Northwestern University specializing in Artificial Intelligence.

I'd love to talk to you about our findings, answer your questions as to our data and methodologies, and maybe even talk a little bit about the voting systems. Mostly other members of the team handle the systems side, so my primary focus is on the data and the story that the data tells.

I'll echo something that another team member (Justin Mealey) posted in his AMA: please forget what you've heard about the poll pads. It's sending people down the wrong paths of analysis, and what Jovan said about the poll pads and hacking into them is not correct.

EDIT: Thank you for all the questions! We've got to get back to the analysis, as you all know what's at stake in finishing this work before the 6th. Take care everyone!

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– BillsmafiaGG 1 point ago +1 / -0

Many thanks for the great work. Can your team demonstrate that negative votes are NOT ordinary?

The objection goes like this:

  1. Shared spreadsheets exist.
  2. Multiple instances of a shared spreadsheet can show staggered updates.
  3. Updates on instance A of shared spreadsheet would appear to “erase” updates from instance B of the shared spreadsheet (“negative” votes detected.)
  4. When synced, updates are reconciled. The “negative” votes are an illusion.

I do not require expertise to make this objection. I can simply ASSERT that somewhere in the pipeline, computers report on a shared basis with staggered updates. Therefore negative votes are ordinary and do not demonstrate fraud.

Unless your team goes on-site and physically verifies otherwise, I’m afraid the objection would be sustained (even if the point is ultimately wrong).

It is essential to demonstrate a change in the ground situation. How does 2020 data compare with 2016? 2018? With data from a region regarded as non-fraudulent? Does the frequency of “negative” votes change?

Without a point of comparison, I’m afraid your efforts can be justifiably waved off as “business as usual.” Just one reference or portion of a slide would help tremendously.

Best of luck to your team!

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