Bayesian Analysis of Random Event Generator Data

Jefferys, William H • 1990 STAR GATE Era • skeptical

📌 Appears in:

⚡ Global Consciousness Project ⚡ Meta Debate

Plain English Summary

The PEAR lab at Princeton collected a massive dataset -- over 104 million trials -- claiming people could mentally nudge random number generators. The results looked statistically impressive using standard methods. But astronomer William Jefferys applied a different statistical lens called Bayesian analysis (which asks "how much should this evidence actually change my mind?") and found something striking: the evidence actually favors there being NO psychic effect. The classic statistical test was overestimating the strength of the evidence by at least 20 times. This is a famous statistical trap called the Jeffreys-Lindley paradox -- when you run enormous numbers of trials, tiny meaningless blips can look significant with standard tests. Jefferys argued these results shouldn't convince anyone who wasn't already a believer, and that parapsychology needs better statistical tools.

Abstract

Data from experiments that use random event generators are usually analyzed by classical (frequentist) statistical tests, which summarize the statistical significance of the test statistic as a p-value. However, classical statistical tests are frequently inappropriate to these data, and the resulting p-values can grossly overestimate the significance of the result. Bayesian analysis shows that a small p-value may not provide credible evidence that an anomalous phenomenon exists. An easily applied alternative methodology is described and applied to an example from the literature.

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📋 Cite this paper

APA

Jefferys, William H (1990). Bayesian Analysis of Random Event Generator Data. Journal of Scientific Exploration.

BibTeX

@article{jefferys_1990_bayesian_rng,
  title = {Bayesian Analysis of Random Event Generator Data},
  author = {Jefferys, William H},
  year = {1990},
  journal = {Journal of Scientific Exploration},
}