Predicting the 2016 U.S. Presidential Election Using a Double-Blind Associative Remote Viewing Protocol
Researchers: Debra Lynne Katz & Michelle Freed Bulgatz, Data Analyst: Nancy McLaughlin-Walter
Publisher: Eight Martinis | Issue 15
Abstract
In this double-blind Associative Remote Viewing Project, 41 moderate to highly experienced Remote Viewers were tasked with describing a feedback photo they would see at a future date. The photo was to be associated with the winner of the 2016 U.S. Presidential Election. Researchers compared the remote viewers’ written transcripts to a set of four photos– two associated with to the Republican and Democratic frontrunners, one with a third-party candidate opinion, and one with an impossible opinion that served as the control group. A formal prediction was issued for a short period with some viewers being exposed to it and some not, to assess whether exposure to a potentially wrong prediction might result in displacement to the wrong photo. Other variables such as viewer preferences and voting behaviors were also assessed. Based on the suggestion to reject the null hypothesis during the hypothesis test summary a Wilcoxon test was conducted to assess the judge’s scoring value of viewer transcript across photos. The results indicated a significant difference where z= -3.147, p<.01. The mean of the ranks of Hilary (the popular vote front-runner) was 13.71, while the mean of the ranks in favor of Trump (the electoral vote front-runner) was 17.28. Results indicated that rather than describing the photo the remote viewers consciously saw at the future date, they instead tuned into photos they would not see. Why did this happen? Is a large group consensus-based approach really the best to use in projects such as these? And what does this mean for the future of Associative Remote Viewing projects that encounter similar incidents of displaced psi despite what seems to be a logical and theoretically sound design?
