According to standard quantum theory, the occurrence of a specific outcome during a quantum measurement is completely random (see Bell 1964). However, some authors refer to revised versions of quantum mechanics (e.g., Penrose & Hameroff 2011, Mensky 2013, Stapp 2017), and propose that the human mind can actually influence the probability of such outcomes. Empirical support for this idea has been provided by micro-psychokinesis (micro-PK) research (see Bösch, Steinkamp, & Boller 2006). However, attempts to replicate these findings have often failed (e.g. Jahn et al. 2000). In an attempt to explain these failures, von Lucadou, Römer, and Walach (2007) established a theoretical model for predicting unsystematic variations of such an influencing effect across replications, resulting in a decline of the effect in micro-PK data over time. Maier, Dechamps, and Pflitsch (2018) slightly expanded this theory by proposing that the temporal variation of such an effect follows a systematic pattern, which can be tested and used for prediction making. In this research we generated such a prediction using data from two previous studies that initially demonstrated a strong micro-PK followed by a subsequent decline in the effect over the course of 297 participants (Maier & Dechamps 2018), we then put it to test with an additionally preregistered set of recollected data from 203 subjects. We compared these results with 10,000 simulated data sets (each set with an N = 203) each comprising random data. Three tests were applied to the experimental data: an area under the curve analysis, a local maximum fit test, and an endpoint fit test. These tests revealed no significant fit of the real data regarding the predicted data pattern. Further analyses explored additional techniques, including an analysis of the highest reached Bayes Factor (BF) over the course of the experiments, the overall orientation of the BF curve, and its transformation into oscillatory components via a Fourier analysis. All these methods allowed for statistically significant differentiations between experimental data on the one side, and the control group and simulation data on the other. We conclude that the analyses of the temporal development of an effect along these lines constitute a fruitful approach toward testing non-random and volatile time trends within micro-PK data.
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