There are thousands of empirical evidences about UFO sightings. However Bayesian interference (increase the credibility) of each of the evidence is very small. That is, most of these evidences have an equal probability of being true or false and do not carry any information.
The credibility of Bayesian evidence is not how close it is to equal probability of true or false. It’s how different the world would look with and without the hypothesis in question.
That is, we look at the world, and say “How many bright-light-in-the-sky-moving-erratically events would we expect if UFOs were not possible?” and compare it to the number of UFO sightings. If the numbers are very similar (they are) then UFO sightings are uncorrelated with UFO existence. Something like 100,000 events that have no relationship with UFO existence doesn’t change the probability of UFOs existing at all.
Now, a harder target. From the document:
Most of what is “known” about UFOs comes from individuals’ descriptions of what they say they saw. If the individuals are reliable and knowledgeable about the sky, the information stands a good chance of being useful. This is the source of the case’s “credibility,”
The extremely common case of people privileging the hypothesis due to availability bias is almost wholly the explanation for individuals “knowledgeable about the sky” to see lights and think “UFOs”.
Radar has played a major role in UFO sightings, repeatedly confirming the presence of something unidentified which responds to radar much as an airplane does. Clouds and other weather phenomena show up on radar, but any experienced operator can tell the difference between weather and something solid.
This is due largely to the author’s lack of understanding of publication bias and false positive rates. If a radar controller fails to identify a signal as an actual aircraft or cloud about one time in ten thousand, and we have 100 million radar events a year, we’d expect about 500,000 radar UFO sightings—five times more than we apparently have,
Physical-traces-cases reduce to burned shrubs and depressions in the dirt. Again, “how many burned-shrub-and-disturbed-dirt events should we expect if UFOs don’t exist”.
The document’s case for UFOs is negligible. If this is the best available evidence, you should not believe in UFOs.
Yes, I mean exactly what you said about Bayesian probability: that UFO evidences do not support UFO existence hypothesis more than UFO non existence hypothesis.
The credibility of Bayesian evidence is not how close it is to equal probability of true or false. It’s how different the world would look with and without the hypothesis in question.
That is, we look at the world, and say “How many bright-light-in-the-sky-moving-erratically events would we expect if UFOs were not possible?” and compare it to the number of UFO sightings. If the numbers are very similar (they are) then UFO sightings are uncorrelated with UFO existence. Something like 100,000 events that have no relationship with UFO existence doesn’t change the probability of UFOs existing at all.
Now, a harder target. From the document:
The extremely common case of people privileging the hypothesis due to availability bias is almost wholly the explanation for individuals “knowledgeable about the sky” to see lights and think “UFOs”.
This is due largely to the author’s lack of understanding of publication bias and false positive rates. If a radar controller fails to identify a signal as an actual aircraft or cloud about one time in ten thousand, and we have 100 million radar events a year, we’d expect about 500,000 radar UFO sightings—five times more than we apparently have,
Physical-traces-cases reduce to burned shrubs and depressions in the dirt. Again, “how many burned-shrub-and-disturbed-dirt events should we expect if UFOs don’t exist”.
The document’s case for UFOs is negligible. If this is the best available evidence, you should not believe in UFOs.
Yes, I mean exactly what you said about Bayesian probability: that UFO evidences do not support UFO existence hypothesis more than UFO non existence hypothesis.