Dawkins vs. Bayes

Recently we've taken a look into several atheist arguments that seem valid on the surface, but are actually circular reasoning.  In particular, we've discussed Richard Dawkins, Bart Ehrman, and David Hume.  Dr. Dawkins assumes God cannot exist in his attempt to prove God does not exist.  Dr. Ehrman assumes miracles are probability zero (i.e., impossible) in his attempt to show that miracles are not accessible historically.  Hume assumes one must see a miracle in order to prove a miracle.  But besides circular reasoning, what do each of these have in common?  Each of their arguments are easily defeated by simply applying rigorous probability theory.



The rigorous probability theory that I speak of is called Bayesian inference, which is a way of assessing how strongly a piece of evidence supports a particular theory.  In Dr. Dawkins's case, when you apply Bayesian inference to the problem of fine-tuning, you must compare two probabilities: the probability that the universe is finely-tuned by accident, and the probability that God exists.  Since the first is so low, the only way to escape the fine-tuning is to retreat to the position that God cannot exist.  Which is what Dr. Dawkins does.  (See here for our previous explanation of this.)  Unfortunately, this is also circular reasoning, a.k.a, being so convinced of your presupposition that no amount of evidence will ever sway you, a.k.a, believing without (or in spite of) evidence, a.k.a., blind faith.