Occam's Razor and Bayesian Measures of Likelihood Suggest Loch Ness Monsters Are Real Animals

How to Cite

Bauer, H. (2023). Occam’s Razor and Bayesian Measures of Likelihood Suggest Loch Ness Monsters Are Real Animals: An Example of Premature Discovery With Implications for Public Policy. Journal of Scientific Exploration, 36(4), 740-748. https://doi.org/10.31275/20222647


Regarding claims of Loch Ness Monsters, what the simplest explanation might depend on how the evidence is assembled and judged. Eyewitness reports can be sim-ply and plausibly explained away as misperceptions and occasional hoaxes. Many of the claimed surface photographs can be simply and plausibly challenged as misleading rep-resentations of natural phenomena as well as some deliberate faking. Simple explana-tions are less readily to hand for the underwater photos and the Dinsdale film, yet disbe-lievers have offered some. However, when the evidence is taken as a whole, the simplest explanation is that there are real animals responsible for these three or four quite inde-pendent types of evidence. A similar conclusion is reached by considering the evidence by a Bayesian approach, progressively modifying the estimated likelihood using each independent type of evidence. How the evidence accumulated matters a great deal: If sonar and photographic evidence had preceded rather than followed intense global interest based on eyewitness reports, the existence of Nessies might have become, by about 1980, widely accepted rather than disbelieved. Loch Ness Monsters might be the sort of premature discovery described by Gunther Stent. The best evidence came too late to influence media attitudes and popular belief. The difficulty of changing long-held views is illustrated not only in this instance but also within science overall, where hegemonic theories have for lengthy periods withstood the accumulation of considerable contradicting facts. Advice to policymakers should come from people who understand such aspects of science as its fallibility and the chanciness of which data come to hand when, thereby determining initial choice of explanations that then resist displacement


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