The basic issue of the paper is to ask whether the observed effect in the remote PEAR data is better modeled by the assumption that the mean is actually shifted in the intentional conditions (influence) or by the assumption that the operator is choosing the intentions to suit the outcome of the remote device's operation (selection). The method is to compare the distributions of run scores in the three intentional conditions (high, low, and baseline) with their "rank frequencies." Rank frequency is the proportion of series displaying each of the six possible relative rankings of the three intentions from highest to lowest outcome. Influence and selection models give different predictions of the functional relationship between intentional distributions and rank frequencies. Selection is refuted at about p=.03; influence is consistent with the observed data. The selection model assumes that the operator somehow becomes aware of the actual run outcomes and assigns intentions to suit, but I also present an argument showing that given the small overall effect size, a standard DAT model would produce the same statistics in the output data as the intention-selecting model that I actually analyzed. (The two models would diverge for substantially larger effect sizes.)

In a more general way, I am skeptical of DAT because it seems inconsistent with the data I have seen, May's meta-analysis notwithstanding. However, the restricted version covered in the JSE paper is the only case I have yet analyzed with any rigor.

York Dobyns

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