• Miracles Again

    A few days ago, I posted “Of Miracles” which was intended to show, in a manner similar to David Hume, that miracle claims typically fall into the realm of the unbelievable. There is one thing that I should have posted but did not.

    In Age of Reason, Thomas Paine says:

    We have never seen, in our time, nature go out of her course; but we have good reason to believe that millions of lies have been told in the same time; it is therefore, at least millions to one, that the reporter of a miracle tells a lie.

    Scholars have spilled quite a bit of ink arguing over what Hume meant when he wrote “Of Miracles” but if Hume was thinking along the same lines as Paine, then Hume’s reasoning was wrong. Here’s why: just because an event is infrequent (even super-infrequent) it does not automatically follow that the reports of such an event are likely to be lies. To use an analogy: suppose that there is a newspaper that reports a certain lottery sequence winning, and the odds of this exact sequence turning up is one in ten million. Suppose also that the paper is known to make a mistake one time in ten thousand (that is, one in every ten thousand facts the paper reports is false). Would it be, to parody Paine, thousands of times more likely that the paper was mistaken than that the exact lottery number was drawn? Can you never trust the newspaper again on things like this? I think not. The relevant question to ask is: when the lottery number is drawn, how often does the paper misreport it? Answer: one time in ten thousand. Therefore you can have high confidence in the paper’s lottery win coverage. Likewise, it wouldn’t matter if miracles represented only one event out of a trillion trillion, what we need to know is this: when a miracle is reported, how often are the reports correct?

    In my previous post I gave one argument to the effect that testimony for events which are unprecedented (in the sense that reports something we have not previously established to be true) carries little force, since we might expect a few people to give such testimony no matter what (a few lie, a few are deceived). I later argued that, as a matter of fact, we know that miracle claims are usually if not always false. It is therefore the case that when a miracle claim is made,  we should expect it not to be true, since the least we know is that most are not.

    I think that some people have taken the whole “heavy burden of proof for miracles” position way too far (I include myself in this category, I too have made the same mistake in the past). Miracle reports are unlikely to be true. In fact, on the basis of the discussion and evidence given in my last post, I think it would be fair to say that, at very least, we know that less than one miracle claim out of a thousand is true. This doesn’t mean that any miracle claims are actually true, only that less than one in a thousand (possibly zero) is correct. With a prior probability of one in a thousand, all you would need to do to show a miracle occurred is to show that there is evidence or ‘datum’ (besides the testimony to which you refer) is over 1,000 times more likely if a miracle occurred than if it did not (Via Bayes’ Theorem). That’s a big burden of proof, but it isn’t impossible to meet, nor is it the case that any natural explanation we can think of would defeat it (just so long as the natural explanation had major flaws when compared to the supernatural one, like explaining the evidence really poorly, being ad hoc, etc.). If you could do even better, and show that the evidence you had was millions of times more likely on the miracle hypothesis than on the no-miracle hypothesis, I think your argument would, could, and should convince even the most hardened skeptic. However, no one to my knowledge has ever met this burden of proof. Care to wager on why that is?

    Category: Uncategorized

    Article by: Nicholas Covington

    I am an armchair philosopher with interests in Ethics, Epistemology (that's philosophy of knowledge), Philosophy of Religion, Politics and what I call "Optimal Lifestyle Habits."