Before I go over why I think the title of this blog post is true, let me go over the case of Oliver's Blood again:

In Bayes' Theorem, the Likelihood Ratio is how likely your hypothesis is in relation to all other hypotheses posited, and also shows how strongly the evidence favors, or disfavors, your hypothesis. This is simply dividing P(E|H) by P(E|~H). Let's say we're presented with the following scenario:Two people have left traces of their own blood at the scene of a crime. A suspect, Oliver, is tested and found to have type O blood. The blood groups of the two traces are found to be of type O (a common type in the local population, having frequency 60%) and of type AB (a rare type, with frequency 1%). Do these data (the blood types found at the scene) give evidence in favour of the proposition that Oliver was one of the two people whose blood was found at the scene?So in this scenario, if Oliver accounts for the O type blood, then one unknown person accounts for the the AB. This gives us the P(E|H) = 1% or 0.01. You may say, at this point, that it fits the hypothesis of Oliver being there, since if Oliver was there, and he left blood, then at least one of the samples of blood would be type O.On the other hand, if Oliver is not guilty, ~H, then this gives us P(E|~H). Or that two unknown people left blood at the scene. What is the probability of finding the evidence at hand if Oliver is not guilty? This means we have two people at random who at each random selection has to account for either the 1% or the 60%. This becomes 2 * 0.01 * .6, which is 1.2% or 0.012.[table]As you can see from this chart, a Likelihood Ratio that is lower than 1 means that it slightly supports the hypothesis that Oliver isnotguilty! Or in other words, there's a higher probability of finding the evidence that we have if Oliver were indeed not at the scene of the crime and two other random people committed the murder. Going back to the simpler form of Bayes Theorem, P(E|H) would be the 0.01 and that is denominated by P(E). P(E) in this case would be the probability of finding the evidence at hand period, which is 2 * 0.01 * .6, which is 1.2% or 0.012. Again, P(E|H) < P(E). So even though the evidenceintuitivelyfits the hypothesis that Oliver is guilty, it is more likely, due to the math implicit in the evidence, that Oliver is not guilty.

So I was just reading over my last post Extraordinary Claims Require Extraordinary Evidence. And like I said in that post, most people are convinced of the truth of their particular religion due to a religious experience. But what is the rate of false positives for religious experiences? What is the probability that if we polled a random person from the planet that their religious experience would be evidence for the "true" religion (whichever is) or the "false" religion?

We can actually get a rough estimate for this. Again, think about the totality of human existence. Most people who have ever lived – from the ancient Greeks, Aztecs, modern Hindus, Buddhists, Jains, etc. – have had religious experiences. To be a Christian (as an example) and think that only Christians have had religious experiences – the inner witness of the Holy Spirit, or the Burning in the Bosom – would be a pretty arrogant claim. Jews and Muslims, as well as Hindus, ancient Mayans, modern Native American shamans, etc., must have all had religious experiences that gave them absolute conviction that their faith is true.

Now if we actually do a rough estimate of all of these religious experiences, both modern and ancient, then from a Christian point of view, the overwhelming majority of religious experiences have actually pointed to the truth of some

*other*religion. Equally so from a Muslim point of view, a Zoroastrian point of view, a Mithraist point of view, and on. This means that our P(E | H), the probability of having a religious experience*given*the truth of some religion, is insanely low, because the vast majority of people who have had religious experiences were not Christians (or Muslims, or Hindus, etc).It would seem that this conditional probability is denominated by a much higher P(E | ~H). In this case, P(E | ~H) would be that physiological hiccup I mentioned in that "Extraordinary Claims…" post. It would even be lower than bare random chance (50%). And if the denominator is larger than the numerator, the Likelihood Ratio is lower than 1. This gives us an equivalent scenario to Oliver's Blood: Religious experiences are actually evidence

*against*the hypothesis of the truth value of some religion, even though it might be intuitive to think the opposite.So not only is the prior probability of the existence of the truth of some religion insanely low to being with, religious experiences actually make that probability

*even lower*.