‘Voter Fraud’ Conspiracy Theorist Believes But Can’t Prove “Illegal” Voting Scandal

As President Trump continues to push his “voter fraud” conspiracy theory on the American people, CNN’s New Day interviewed Votestand founder Gregg Phillips, one of the men who believes that Democratic presidential candidate Hillary Clinton somehow received over 3 million “illegal” votes.

Phillips actually made his claims of “voter fraud” via Twitter long before the certified results of the election were made public.

This is the tweet that caught Donald Trump’s attention three days after the election.

And while confident of his theory, he tells CNN’s Chris Cuomo he doesn’t currently have the evidence to prove it.

From Rawstory:

“You say you can prove it,” Cuomo said of Phillips’s voter fraud claims. “I say, ‘OK, I trust you, you can prove it.’ Show me.”

“We believe it will take probably another few months to get this done,” Phillips said of when his team will be ready to publicly offer proof that he insists is ironclad.

“Even though you can’t prove it, you think you know?” a confused Cuomo asked.

“The numbers are actually bigger!” Phillips replied, apparently implying that Hillary Clinton actually received more than 3 million illegal votes.

Cuomo again pressed Phillips for how he could claim to know for certain that Clinton received millions of illegal votes without currently having the proper evidence to back up such a conclusion.

“You can reach a conclusion, and then still verify it,” Phillips responded.

Elsewhere in the segment, Cuomo questioned Phillips’s purported methods for finding voter fraud were at all precise, and he responded by saying, “We are as precise as we need to be.”

No sane person believes that 3 million people somehow fraudulently worked in a concerted effort to elect Hillary Clinton president. IF such an undertaking took place, why would the focus be on California and New York where Hillary was certain to win anyway, and not in swing states like Florida or Pennsylvania?

Watch the CNN interview below: