Hume's Four Suplemental Arguments Against Miracles Vs Michael Licona

As mentioned in a previous post, David Hume argued that the prior probability of a miracle is infinitely low because it violates established laws of nature. Consequently, the likelihood of human testimony being mistaken will almost always outweigh the probability of the miracle itself, making it irrational to believe.

But what's often neglected is that Hume offered four supplemental arguments that discussed the "likelihood" and "false positive rate" of miracles in Part 2 of Chapter 10 of the Enquiry. This part of Hume's chapter comes after he presented his general disproof of miracles in Part 1. I think Hume didn't revise his chapter after learning about Bayes, because he had already used the structure of Bayes in his chapter! In the first part he presented his philosophical disproof against the abject improbability of human testimony for miracles that must overcome the laws of nature. This burden is made heavier when considering uncorroborated miracle claims for which we cannot interogate the alleged people mentioned in the biblical texts. After that destruction what else did Hume need to say in Part 2? But he had more to say.

Here's an excerpt from my long chapter on miracles in Why I Became an Atheist (2012), titled, Hume's Four Suplemental Arguments Against Miracles. My focus is on Michael Licona's arguments against Hume. I think I could do a better job of this if you combined other chapters I mentioned. If you want to read the whole chapter with endnotes you'll have to get the book. What? You haven't done so yet? ;-)

A.I. On Bayes Theorem Stressing David Hume's Contribution!

"Is Bayes theorem a legitimate tool to evaluate miracles in the Bible?" 

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Yes, Bayes’ Theorem is a legitimate mathematical and philosophical tool for evaluating biblical miracles. However, because the theorem requires subjective inputs—like the initial probability of a miracle occurring and the reliability of ancient texts—scholars debate how definitively it can prove or disprove specific events. [1, 2, 3]

The Bayesian Formula:

Bayes’ theorem calculates the probability of a hypothesis (H) being true given certain evidence (E).

The Components:

Evaluating miracles using this theorem involves three key factors:

[1] Prior Probability P(M):

Your initial assessment of the likelihood of a miracle occurring before considering the specific evidence. Skeptics often argue this is near zero, while proponents argue it is higher based on the existence of an active, intervening Creator. [1, 3, 4]

Likelihood P(T|M):

The probability that we would have the existing historical records and testimony if the miracle actually happened. [1]

False Positive Rate \(P(T\vert{}\sim M)\):

The probability of getting that same testimony or record even if the miracle did not happen (e.g., due to human error, legend, or deception). [1]

The Debate in Scholarship:

While the method itself is logically sound, applying it to the Bible sparks intense scholarly disagreement: [1, 2]

The Skeptic's View:

Philosopher David Hume argued that the prior probability of a miracle is infinitely low because it violates established laws of nature. Consequently, the likelihood of human testimony being mistaken will almost always outweigh the probability of the miracle itself, making it irrational to believe. [*See Below]

Bayes Theorem and Miracles: Three Cases Studies:

Philosopher Michael P. Levine writes a bit eccentrically about miracles but he also has some incisive things to say. For people who think David Hume’s arguments against miracles are obsolete and should be ignored, Levine says, “The locus classicus for modern and contemporary philosophical discussion of miracles is Chapter X (‘Of Miracles’) of David Hume’s Enquiries.”[1] [“Locus classicus is a Latin term used in literature, law, and other academic fields. It refers to the original or most authoritative passage or source for a particular word, phrase, concept, or idea. In other words, it is the classical or standard example that is most often cited as the definitive reference for a subject.”] So he proceeds to analyze what Hume wrote.

He tells us “there are various versions of Bayes’s theorem,” not just one, depending on the hypothesis to be resolved. With miracles they are many. He highlights John Earman’s formulation:

“The reader is invited to think of H as a hypothesis at issue; K as the background knowledge; and E as the additional evidence. Pr(H/E&K) is called the posterior probability of H. [i.e., the Probability of the Hypothesis divided by the background Knowledge]. Whereas Pr(H/K) and Pr(E/H&K) are respectively called the prior probability of H and the (posterior) likelihood of E.”[2]

Labeling this as “Hume’s argument” Levine adds: 

On the interpretation of Hume’s argument given above, a Bayesian analysis sheds no light whatsoever on the structure or substance of the argument, and can do nothing by way of either supporting or refuting the argument. Indeed, any Bayesian analysis of the question of justified belief in miracles must be otiose until the difficult and essential questions concerning “evidence” in relation to an allegedly miraculous occurrence are resolved - at which point any Bayesian analysis will add little except the technical complexity of a formal apparatus that may or may not “clarify” the structure of Hume’s argument. The balancing of probabilities is of no use until it is decided what goes into the balance - that is, what constitutes the evidence.[3]

Let's Highlight the Evidence (E). This is the crucial factor regardless of one’s background knowledge (K) or priors. Regarding a Christian Bayesian analysis, Levine comments, “The idea that the Bible provides historically acceptable grounds for believing in miracles reflects a wish-fulfilling rendering of what constitutes evidence.”[4] Everything hangs on the quantity and the quality of the evidence. It’s the indispensable input, the essential element, and the non-negotiable necessity.

On Miracles and the Importance of Contexts, Perspectives, Priors, Background Information, and Bayes Theorem

--Norman Geisler, a Christian apologist, is on record as saying:

The mere fact of the resurrection cannot be used to establish the truth that there is a God. For the resurrection cannot even be a miracle unless there already is a God. . . . The real problem for the Christian apologist is to find some way apart from the mere facts themselves to establish the justifiability of interpreting the facts in a theistic way. . . . No fact, event, or series thereof within an overall framework which derives all of its meaning from the framework can be determinative of the framework which bestows that meaning on it. For no fact or set of facts can of and by themselves, apart from any meaning or interpretation given to them, establish which of the alternative viewpoints should be taken on the fact(s). [Norman Geisler, Christian Apologetics (Grand Rapids, MI: Baker House, 1976), see pp. 94–98.]

Geisler's perspective stands in stark contrast to other ways of knowing whether Jesus arose from the dead. Geisler was endorsing Classical apologetics. Evidentialist apologetics by contrast, basically rejects it, since it seeks first and foremost the evidence.

--I. Howard Marshal. According to Christian scholar Marshall in his book, I Believe in the Historical Jesus (Eerdmans, 1977) “many historians—the great majority in fact—would say that miracles fall outside their orbit as historians. For to accept the miraculous as a possibility in history is to admit an irrational element which cannot be included under the ordinary laws of history. The result is that the historian believes himself justified in writing a ‘history’ of Jesus in which the miraculous and supernatural do not appear in historical statements. The ‘historical’ Jesus is an ordinary man. To some historians he is that and no more. To others, however, the possibility is open that he was more than an ordinary man—but this possibility lies beyond the reach of historical study as such.” (p. 59).

--William Lane Craig is on record as saying:

"J.L. Mackie's critique of miracles is particularly shockingly superficial."

Craig said this in his paper, The Resurrection of Theism, Endnote # 19. Craig was commenting on the late J.L. Mackie's book, The Miracle of Theism. Craig was prompted to say this while reading Alvin Plantinga’s critique of it titled, Is Theism Really a Miracle?, in Faith and Philosophy, [April 1986].

--J.L. Mackie by contrast argued against the belief in miracles. Is he really "shockingly superficial"? Let me quote from him since it's so good:

Why Didn't David Hume Respond Upon Knowing Bayes Theorem?

I'm currently trying to resurrect David Hume from the dead. We've established that David Hume knew of Bayes Theorem and had two chances to edit his chapter "Of Miracles" in response, but didn't. This seems to be "a deliberate philosophical position," per William Vanderburgh.

As an author I've published a great many blog posts, papers, and books about a whole lot of issues; a small encyclopedia of them, if you will. The more that one publishes the more of a context informed readers have for understanding an author on a separate issue. So let's consider the relevant books of David Hume, apart from his History of England which was a massively influential six-volume historical work that thrust him into fame. Let's see if there's a reason he didn't respond to Bayes Theorem. (In what follows A.I. insisted that I let her help a bit.)

A Treatise of Human Nature was published in London in multiple volumes between 1739 and 1740. Together it was Hume’s most comprehensive philosophical work. It breaks down the origins of human knowledge, ideas, cause and effect, personal identity, the passions, and morality.

An Enquiry Concerning Human Understanding (1748). A more accessible, revised version of the first book of the Treatise. It specifically addresses skepticism, our understanding of cause and effect, free will, and the critique of miracles. Thomas Bayes’s work on probability appeared posthumously in 1763. His friend Richard Price discovered the manuscript and published it posthumously in 1767 along with his explanation of Bayesian methods for assessing miracles. We know Hume read Price's paper and liked it. But Hume neither addressed Bayesian arguments nor revised his account of miracles for the 1768 and 1777 editions of his Enquiry.

An Enquiry Concerning the Principles of Morals (1751): Hume’s dedicated work on ethics. By relying on empirical observations of human nature rather than abstract religious or rationalist dogmas, Hume presents morality as a practical, natural tool designed by humans to foster social harmony and mutual well-being.

The Natural History of Religion (1757). Hume treats religious history as a natural, secular phenomenon, fundamentally undermining the belief that religion is inherently rooted in reason or divine revelation.

Dialogues Concerning Natural Religion (1779). His last book published posthumanously. It explores the psychological and sociological origins of religious belief, arguing that religion stems not from rational contemplation or innate knowledge of God, but from the emotional drives of human nature, primarily fear, anxiety, and the desire for security.

An Excerpt from My 2019 Book, "The Case against Miracles"

David Hume’s arguments against miracles succeed despite the fact he didn’t use Bayes’ Theorem. William Vanderburg briefly addresses this issue in his article for Hume Studies.[1] He shows Hume was aware of Bayes’ Theorem but didn’t think he needed it to make his argument. 

Earman chastises Hume for being unaware of Bayesianism and of mathematical probability generally. This is unfair on two counts. First, Bayes’s work on probability was not widely known in 1748 when Hume published the first edition of the Enquiry. Richard Price arranged the posthumous publication of Bayes’s essay only in 1763, and it remained obscure even after its publication; Price’s paper applying Bayesian methods to the evidence for miracles appeared in 1767. We know Hume read and admired that paper, but he neither addressed Bayesian arguments nor revised his account of miracles for the 1768 and 1777 editions of the Enquiry. This suggests that Hume ultimately did not view Bayes’s work as relevant to the argument against miracles. Second, Hume’s discussion of the probability of chances “shows without controversy that he was familiar with the basic concepts of probability based on the calculus of chances.” Given Hume’s familiarity with Pascalian probability in general, and his acquaintance (through Price) with Bayesian ideas, his non-numerical treatment of the evidential probability of miracles must be seen as a deliberate philosophical position, not as a result of negligence or ignorance.[2]

         What Hume is aiming at is seen in his general maxim: “Therefore we may establish it as a maxim, that no human testimony can have such force as to prove a miracle, and make it a just foundation for any such system of religion.” His twofold contention is not only that mere testimonial evidence for a miracle is insufficient for believing in a miracle, but also that miracles cannot be an adequate foundation for a religion. Hume is undercutting miracles and any religion born of miracles in one fell swoop. Going for the jugular vein of miracles does all the work, for it also deals a death blow to the religion of any miracle working god.

If miracles are the foundation for a religion, then an apologist for that religion cannot bring up a miracle working god to establish his supposed miracles. For miracles are supposed to be the basis for the religion and its miracle working god. One cannot reason backwards from the existence of their god to the miracle testimonies in the Bible. For the issue is whether the god of these miracles exists in the first place.

William Vanderburgh On "Hume’s 'Abject Failure' Vindicated"

[First published in Dec. 2018] William L. Vanderburgh defended Hume against John Earman in a very thorough article published in 2005 in Hume Studies, titled, "Of Miracles and Evidential Probability: Hume’s 'Abject Failure' Vindicated." [You can read the PDF right here.] Go read it, now! In it Vanderburgh shows David Hume probably knew of Bayes Theorem and never mentioned it for good reasons. 

I'm including a few of the important highlights below. I consider it an important contribution on Hume, Earman, and Bayes Theroem.

Bayes Theorem with Regard to Vincent Torley

I previously mentioned knowing only one Christian who utilized Bayes and calculated a lower probability for the resurrection of Jesus than before. He went from a 60% probability to 50% to 25% probability. He still believes because after all, it's faith. All other Christians who use Bayes end with their probabilities well into the probability zone, some as high as 95%!

That's what I see. All but one Christian who utilized Bayes comes away with a high probability that Jesus arose from the dead, or that Christianity is true, or that God exists. On the surface Bayes only helps believers confirm their faith. Bayes ought to help believers re-think their faith, but it does no such thing. Granted, not much can change the diehard believer. But my point is that there is something wrong with an allegedly helpful theorem that doesn't force more believers to change their minds [edit: or the minds of non-believers for that matter]. If using Bayes won't help them [edit: or us] why bother?

The Christian believer I mentioned is Vincent Torley. You can read his cogitations on the matter at The Skeptical Zone. Here's his conclusion:

Since my estimate of the total probability of the various Type A skeptical explanations is less than 50%, and since the posterior probability of the Resurrection is much greater than that of the various Type B explanations, belief in the Resurrection is rational, from my perspective.

Based on the evidence, I estimate that there’s about a 60-65% 55-60% chance that Jesus rose from the dead. That means I accept that there’s a 35-40% 45-50% chance that my Christian faith is wrong. [Notice his lowering edits?]

However, I can understand why someone might rate the probabilities of hypotheses 3(a), 3(b) and 3(c) at 20% each, instead of 10%. For such a person, belief in the Resurrection would be irrational, since the total probability of the Type A skeptical hypotheses would exceed 50%.

Summing up: a strong case can be made for the reality of Jesus’ Resurrection. However, a responsible historian would not be justified in asserting that Jesus’ Resurrection is historically certain. As we’ve seen, such a conclusion depends, at the very least, on the claim that there is a significant likelihood that there exists a supernatural Being Who is capable of working miracles, which is something the historian cannot prove. In addition, estimates of the probabilities of rival hypotheses will vary from person to person, and there seems to be no way of deciding whose estimate is the most rational one.

Later, after reading through Michael Alter's book, Resurrection: A Critical Inquiry, Torley came to calculate the probability of the resurrection of Jesus to be 20-25%. LINK But here is the rub that doesn't feel too good. Torley was not prompted to change his mind due to any Bayesian analysis since Alter doesn't use it! Alter merely hammered home a comprehensive evidential case! Checkmate Bayesians!

Bayes Theorem On Trial

Only one Christian believer I know who used Bayes, went from 60% probability to 50% to 25% probability. He still believes because after all, it's faith. All other Christians who use Bayes end with their probabilities well into the probability zone, some as high as 95%!

That's what I see. All but one Christian who utilizes Bayes comes away with a high probability Jesus arose from the dead, or that Christianity is true, or that God exists.

On the surface Bayes only helps the believer confirm his or her faith. Bayes ought to help believers re-think their faith but it does no such thing.

Bayes On Trial: The Problem of Bayes in a Nutshell

In a nutshell what's wrong with using Bayes Theorem: Using it to determine the probabilities of miracles in the ancient pre-scientific world. The problem is the attempt to take miracles seriously. The biblical authors never thought anyone could prove they happened as recorded.

Bayes Theorem On Trial!

BAYES ON TRIAL! Imagine trying to convince someone of Einstein's equations, Isaac Newton's second law of motion (f=ma), or of biological evolution, by using Bayes Theorem. We never do that, nor do we use it on a great many other evidence based issues. The objective evidence is convincing all on it's own.
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A.I. on Bayes Theorem. I typed in these words: "Criticisms of bayes theorem theory"
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While Bayes’ Theorem is an undisputed mathematical formula, criticisms center on its application in Bayesian statistics and epistemology. The primary debates involve subjectivity in prior beliefs, methodological ambiguity, and computational/logical limitations.

1. The Subjectivity of Prior Probabilities (The Problem of the Priors). Bayes' theorem requires defining a "prior"—an initial assumption or belief about the probability of an event. Critics, particularly classical/frequentist statisticians (e.g., Fisher and Neyman), argue that because these initial beliefs are subjective, the resulting probability is also subjective, making it scientifically unreliable. If a prior is heavily biased or uninformed, it can take an excessive amount of new data to correct the final outcome.

2. Lack of Uniqueness (The Objective Bayes Dilemma). To counter subjectivity, "Objective Bayesians" use uninformative priors (representing ignorance). However, critics argue there is no single mathematically rigorous way to define a "true" state of ignorance. Depending on the mathematical method chosen, the resulting prior can inadvertently introduce hidden assumptions that distort the analysis.

3. Falsifiability and Flexibility. In scientific modeling, the sheer flexibility of Bayesian frameworks (with choices of priors, likelihoods, and utility functions) allows researchers to continuously adjust models until they achieve desired results. Critics argue this makes Bayesian theories susceptible to becoming unfalsifiable, or used to spin "just-so stories" to validate pre-existing conclusions rather than testing them objectively.

4. The Bayesian Trap and Pseudoscience. In applied settings, misinterpreting the odds or failing to seek alternative explanations can lead to over-diagnosis and confirmation bias. Practitioners can fall into the "Bayesian trap", where strong prior assumptions or repeated observations lead to a false sense of certainty, blinding the researcher to anomalies or the need for new experiments.

5. Logical Omnipotence and Unification. When applied to human reasoning or epistemology, critics point out the problem of logical omnipotence. Pure Bayesian theory assumes that a perfectly rational agent instantly assigns probabilities to all possible alternative hypotheses and knows all logical consequences of their beliefs, which is an impossible cognitive burden for actual human beings or finite computers.

6. The "Bayesian Brain" Circularity. In cognitive science, the Bayesian brain hypothesis suggests that perception and learning are based on probabilistic inferences. Critics argue this approach can be ambiguous and methodologically circular—suggesting that because a system "looks" rational, it must be using Bayesian calculations, despite a lack of neurological evidence for such complex mathematics at the process level.

Quote of the Day On Bayes Theorem by josephpalazzo

josephpalazzo replied in Debunking Christianity: Here's Bayes Theorem:

In the denominator P(B) must refer to actual data, not some possibility. This also goes for all the other variables P(AlB), P(BlA) and P(A). As example, what is the probability of turning head on tossing a fair coin, which is 1/2. That number, 1/2, can be verified by tossing a fair coin 1000 times, 10,000 times and so on. It's not a guess, nor some divine revelation of some desirable event. If there is no verifiable data, Bayes Theorem is totally useless..

Additional Thoughts On Using Bayes' Theorem

No one should expect that a good argument is one that convinces reasonable people. What we should expect is that an argument is a good one, or a strong one, or very strong one, irrespective of whether it is a convincing one. Even though I know this, I still try to come up with arguments that are convincing to most reasonable people. I expect kickback from Christian believers. What can annoy me is kickback from other atheists and agnostics, especially if they don't let it go after a while, until they say nothing new I haven't considered before. BTW: A person can annoy me on one issue but be very informative, completely delightful and insightful on most everything else. That describes Ignorant Amos. In fact, the commenters here seem to be the best I've seen anywhere!

I have defended the use of Hitchens’ Razor over the use of Bayes’ Theorem (BT) when assessing miracles like a virgin birthed deity and the resurrection of Jesus. I have argued that BT cannot and should not be applied to claims which are nonsense, and that miraculous claims in the ancient Biblical past are all nonsense! They are all nonsense because there is absolutely no credible evidence for any of them. I have also argued that the goal of atheists should be to change minds, and that fewer minds are changed the more we respond with greater and greater sophistication. Doing so also legitimizes nonsense by giving believers undue credibility. I agree with philosopher Julian Baggini who said, "Converts are won at the more general level." [infidels.org/kiosk/article.] For responding to fundamentalist philosophy only encourages fundamentalist philosophers. On the general level even ridicule changes minds.

I don’t object to using BT when it’s applied appropriately to questions for which we have prior objective data to determine their initial likelihood, along with subsequent data to help us in our final probability calculations. It’s an excellent tool when these conditions obtain. So a new provocative question arises, one I didn't address: What is the best tool for assessing the possibility that a historical person existed behind the Jesus character in the Gospels?

Religious Studies On William Vanderburgh's Book, "David Hume On Miracles, Evidence, and Probability"

Religious Studies recently reviewed the book by William Vanderburgh: David Hume on Miracles, Evidence, and Probability. In the Appendix to my own book on miracles I reviewed it very favorably as well. Here are some snippets from the short review:

In David Hume on Miracles, Evidence, and Probability William L. Vanderburgh presents a concise defence of David Hume’s ‘Of Miracles’. By providing a more in-depth look at the relevant biographical details of Hume as well as an expanded investigation of Hume’s broader epistemology, Vanderburgh argues that many commentators, both historical and contemporary, have either misunderstood or misrepresented Hume. At the heart of Vanderburgh’s defence of Hume is the rejection of the arguments put forth by Hume commentators such as Richard Price, John Earman, and others who have attempted to interpret ‘Of Miracles’ from a Bayesian perspective. Vanderburgh argues that approaching Hume’s epistemology from this perspective is fundamentally wrong and that Hume’s argument ought to be interpreted using a non-mathematical probability framework.

What’s Wrong With Using Bayes Theorem to Evaluate Miracles?

In a previous post I spoke on the topic, Miracle Claims Asserted Without Relevant Objective Evidence Can Be Dismissed. Period! At the end I had some closing thoughts about Bayes Theorem and miracles. I'm highlighting it for thought below.

What’s Wrong With Using Bayes Theorem to Evaluate Miracles?

Now I want to end by talking briefly about Bayes Theorem. In his writings and talks Richard Carrier does a good job of explaining it.

Miracle Claims Asserted Without Relevant Objective Evidence Can Be Dismissed!

I recorded a video talk for two virtual conferences this past Labor Day weekend, for the International eConference on Atheism, put on by the Global Center for Religious Research, and for the Dragon Con Skeptic Track. I'm very grateful for these two opportunities. That video will be released sometime soon. In what follows is the text of my talk. Please share if you want others to discuss it with you. Enjoy the discussion!

Today I’m arguing, along the same lines as Christopher Hitchens did, that “What can be asserted without evidence can also be dismissed without evidence.” [God Is Not Great: How Religion Poisons Everything (New York, Twelve. 2007), p.150.] Specifically I’m arguing that “Miracle Claims Asserted Without Relevant Objective Evidence Can Be Dismissed. Period!”

I think all reasonable people would agree. Without any relevant objective evidence miracle claims shouldn’t be entertained, considered, believed, or even debunked. I intend to go further to argue that as far as we can tell, all, or almost all miracle assertions, lack any relevant objective evidence, and as such, can be dismissed out of hand, per Hitchens.

Whose Abject Failure? William L. Vanderburgh Tweets On Hume and Bayesianism

I reviewed Dr. Vanderburgh's book in defense of David Hume in the Appendix to my anthology, "The Case against Miracles." [Click on his book image to find out more.] Amazingly, Vanderburgh sums up his conclusion in one short Tweet! Tim McGrew, supposedly an "international expert" on miracles (but not my expert!), is in the dark on how to understand David Hume on miracles.

Hypothesis: Since Bayes Theorem Cannot Help Us It Should be Abandoned

Here is the full title to this post:

Hypothesis: Since Bayes Theorem (i.e., the math, the equation, the formula) cannot help bring us to a consensus concerning something accepted on faith, or assess specific miracles and theistic based religions, and because it is ripe for abuse in the hands of Christian apologists who dress up their delusion with undeserved respectability, it should be abandoned for better alternative methods, by people who really want to know the truth.

This is not a case of throwing the baby out with the bathwater. There is no miracle baby to be found in the dirty bathwater. Bayes is used by people in this debate who wish to look superior than others. It's a rite of passage into a specific club of intellectuals who like the status of being considered above the rest of us. But it solves nothing, clarifies nothing, and will be thrust into the dustbin of elite faddishness as one after another intellectual wannabe comes up with their own calculations without reaching a consensus between believers and non-believers on the inputs or the resulting probabilities. As philosopher Godfrey-Smith put it, “The probabilities” in Bayes’ Theorem “that are more controversial are the prior probabilities of hypotheses, like P(h).” He asks, “What could this number possibly be measuring?” He says, we cannot “make sense of prior probabilities” [Theory and Reality: An Introduction to the Philosophy of Science (University of Chicago Press, 2003), p. 205]. He is dead on in the area I'm arguing, faith-based claims of virgin birthed deities and resurrections from the dead. And while I'm at it, gods themselves, who are supposed to exponentially increase the prior probabilities.

Bayes is a mathematical wasteland when applies to these issues. The only merit it offers is the discussion of the evidence and the ensuing arguments in defense of the inputs, which could be done without the math. So atheist apologists who argue for the use of Bayes Theorem in an area with no promise or hope of a consensus, are merely arguing for their own special status in these debates, and dividing people unnecessarily between Bayes users and non-Bayes users. The most extreme case of this is atheist apologist Richard Carrier, who thinks the rest of us are ignorant, stupid, and irrational to disagree. This only makes him feel relevant by arguing for his own irrelevancy. This is not to throw a bone at Christian apologists. I think Carrier is brilliant and has already dealt some significant death blows to the Christian faith. But on this issue his brilliancy, and undeserved superior ego, has led him to defend an irrelevant wasteland, a dead end, one that has no promise of accomplishing or solving anything.

The better tools? Science; requiring sufficient collaborative objective evidence commensurate with the type of claim; requiring claimants to shoulder the burden of proof; arguing from inference to the best explanation; using the standard of the Outsider Test for Faith; ridicule (after all, we know faith-based arguments are special pleading all the way down), and more. Carrier will respond just as believers do when it comes to their faith-based doctrines, by forcing these tools into the grid of Bayes Theorem and calling me a doofus another dozen times or more. So let's see this in practice, a friend comes up to you and says his wife gave birth to a deity. You say show me some objective evidence. We don't need Bayes at all there, do you see? I can understand why Bayesian reasoning without the math is much better when it comes to more complicated issues, but at rock bottom it's all about the evidence, just as apologist Vincent Torley was convinced by it, even though he had previously done his own Bayesian calculations. I see no reason why hammering home the lack of objective evidence won't work as well, or better than using Bayesian math. Bayes is probably worse off in terms of convincing others, for the only people who would slough through it are far less likely to be convinced by it. I've written a book on why responding to fundamentalist arguments in kind gives their beliefs a certain undeserved respectability. So my arguments against the use of Bayes are rooted there, but not found exclusively there. For as you can see I have other arguments that Bayes just doesn't help us (i.e., the math, the equation, the formula). [See Tag for more]

How Not to Be a Doofus about Bayes’ Theorem From Someone Who "Doesn't Really Understand Bayesianism"

The title is a response to two posts Richard Carrier wrote here, and recently here. If anyone disagrees with Carrier we're irrational, ignorant, foolish, and now with a newly released super-bad description, doofus/doofuses. 

I would like to catalog the variety of responses apologists and atheists have toward Bayes, but I won't. What I do know is apart from the people he mentions who "don't understand Bayes" he should also include David Hume, Apologist Michael Licona and Dan Lambert. One wonders if anyone could have argued for anything before Bayes given Carrier's praise. Pffft. What I know is that those who use Bayes come up with wildly different results with regard to the resurrection of Jesus.

--Apologist Richard Swinburne calculates the probability of the bodily resurrection of Jesus, given the existence of a god, is 97%. Swinburne should run that past a peer-review panel including Muslims Jews and Hindu's to see how that goes over. ;-) We know from a historian's perspective that's utterly idiotic! 

--Apologist Vincent Torley calculated that "there’s about a 60-65% chance that Jesus rose from the dead." Of course, that was before he read Michael Alter's book on the resurrection, which I recommended, that had no math in it at all! How could this happen without Bayes? Oh my! But it did. Apparently the shear evidence Alter presented was enough. Wow! Who would have thunk it. 

--Apologists Timothy McGrew and Lydia McGrew calculated the odds of the resurrection of Jesus to be 100,000,000,000,000,000,000,000,000,000,000,000,000,000,000 to 1. *Silence* *Awe* *Respect* Christians must revere them for coming up with the highest calculation any intellectual *cough* has done so far. Can anyone do better here? They need to go see a doctor and get some meds, quickly. Richard Carrier thinks Bayes helps. Okay then. Please tell us how such a useful tool can produce these wide diverse results. Tools are supposed to help. But even among apologists themselves it does no such thing. Carrier says Bayes helps us clarify where we disagree and by how much. Really? We already know this! Dressing up a delusion in math is still a delusion. Responding in kind only gives a delusion an undeserved respectability. This is a major point of mine in Unapologetic: Why Philosophy of Religion Must End. Who's the doofus again? 

In Defense of Hume Part 5, John Earman Didn't Refute Hume, He Completed Him

It's widely touted that in his book "Hume's Abject Failure" John Earman "refuted" Hume. Did he? Consider what Richard Carrier tells us:

Earman didn't "refute" Hume, so much as he fixed Hume. Hume wrote just a few years before Thomas Bayes solved the problem Hume was beating around the edges at in his Argument against Miracles. Earman shows that reframing Hume's argument in a Bayesian framework fixes everything wrong with the original argument as worded. Hume's mistake is subtle, and arises from the imprecision of his wording and formulation. He hadn't quite known yet of the correct logical form of what he was trying to say, but it is remarkable he came very close to the same insight his contemporary Thomas Bayes did. Earman's fix rehabilitates Hume's argument...

There are definitely some of Hume's arguments that are spot on, that on their own show miracles cannot be believed based on testimonial evidence alone, especially if one is using testimonial evidence to prove a god exists and his religion is true, when compared to the laws of nature represented by Newton's laws of motion, as I argued here. At best one should suspend judgment. But more than this, Hume is not to be considered wrong, just incomplete, and that's a huge difference.

We just need to consider scientific revolutions. Paradigm changes build on each other as science progresses. The previous paradigms aren't to be considered wrong, but rather incomplete. As science progresses we recognize that the science of yesterday was not yet complete. That's it. If you've never read much of Isaac Asimov's, read his essay called The Relativity of Wrong. It will forever change how you view science. He explains why the discredited science of the past is not to be considered wrong, but rather incomplete, by discussing the changing views of the shape of the earth, from flat to spherical to pear-shaped. The same things can be said about Newton's laws of motion as completed (not falsified) by Einstein's relativity equations. Newton's equations were not wrong, even though he didn't factor time into them, as Einstein did. They just don't work at or near the speed of light. So there's no overturned or falsified theory here! In a like manner, Hume gave us the initial paradigm to evaluate testimonies to miracles which still holds true, but now Earman and others are offering other ways to examine miracles from a more complete paradigm. So no, Hume has not been refuted. He is being completed.