It must have, because he is saying over 130k. It's one thing if you get a few hundred hits. That could be dismissed. However, if you're into the thousands with FULL name and DOB being exact, that's NOT a coincidence.
Also, if that matches and they are all registered voters, AND all voted for Biden... that's basically statistically IMPOSSIBLE to happen naturally.
In Oregon, all you need to log in to your account to request absentee ballots is first, middle, and last name, along with DOB. They use that as log in credentials, so it's not that common to have THOUSANDS of people share those same details.
Not necessarily, but of 130k matches I'd put literally all of the money in my bank account on at least 100k being the same people, or positive matches.
I just did a search across 3.6 million PA residents, grouping by first name, mi, last name, and dob and got 30K pairs. They seem to have different addresses. This dataset (unlike ones I used to work with) is supposed to be heavily processed to remove duplicates.
Only .8% but a big number because the denominator is so large. Dunno the cause, could still be bad data I suppose.
Then my datasets must have been garbage.
It must have, because he is saying over 130k. It's one thing if you get a few hundred hits. That could be dismissed. However, if you're into the thousands with FULL name and DOB being exact, that's NOT a coincidence.
Also, if that matches and they are all registered voters, AND all voted for Biden... that's basically statistically IMPOSSIBLE to happen naturally.
In Oregon, all you need to log in to your account to request absentee ballots is first, middle, and last name, along with DOB. They use that as log in credentials, so it's not that common to have THOUSANDS of people share those same details.
Not necessarily, but of 130k matches I'd put literally all of the money in my bank account on at least 100k being the same people, or positive matches.
That's being very, very generous.
I just did a search across 3.6 million PA residents, grouping by first name, mi, last name, and dob and got 30K pairs. They seem to have different addresses. This dataset (unlike ones I used to work with) is supposed to be heavily processed to remove duplicates.
Only .8% but a big number because the denominator is so large. Dunno the cause, could still be bad data I suppose.