{"id":534,"date":"2007-10-22T10:23:26","date_gmt":"2007-10-22T10:23:26","guid":{"rendered":"http:\/\/scientopia.org\/blogs\/goodmath\/2007\/10\/22\/the-faith-equation-part-two-of-the-review\/"},"modified":"2007-10-22T10:23:26","modified_gmt":"2007-10-22T10:23:26","slug":"the-faith-equation-part-two-of-the-review","status":"publish","type":"post","link":"http:\/\/www.goodmath.org\/blog\/2007\/10\/22\/the-faith-equation-part-two-of-the-review\/","title":{"rendered":"The Faith Equation: Part Two of the Review"},"content":{"rendered":"

The bulk of this part of the review is looking at the total train-wreck that is chapter 4, which contains Bittinger’s version of dreadful probabilistic arguments for
\nwhy Christianity must be true. But before I do that, I need to take care of one loose
\nend from part 1. I should have included chapter three in part one of the review<\/a>, since it’s really just a continuation of the paradox rubbish, but I didn’t.<\/p>\n

<\/p>\n

The basic idea behind chapter three is that Jesus is the most fundamental
\nresolution of paradox. All of the most important of the (Bittinger) paradoxes that we
\nencounter in our lives are ultimately resolved by Jesus. Oh, joy! It’s really just more of the same: he keeps playing with his own personal definition of paradox, and using it to create fake problems that he can solve with cheap arguments and fake math. The only thing really worth pointing out specifically
\nfrom this chapter is another thoroughly pathetic example of how Bittinger appropriates mathematical
\nnotation to lend a sense of credibility to a piss-poor argument:<\/p>\n

lim L(t) = God, where L(t) = life’s spiritual experiences<\/p>\n

What purpose does this “equation” serve? Does it clarify anything? No. Does it explain anything? No. Is there anything that’s meaningfully represented by a function<\/em>? No. Is there anything, of
\nany sort, to be gained by inserting a make-believe function?<\/p>\n

Yes. It looks good<\/em>. Instead of just being more of Bittingers self-obsessed babbling,
\nit looks like<\/em> he’s using his skills as a mathematician to support his arguments, even though
\nhe isn’t. It’s pure nonsense, wrapped up in mathematical notation, just to make it look as if there was something serious there, rather than the total bullshit.<\/p>\n

The other notable thing about this chapter is that Bittinger uses his notion of
\n“paradox” to define sin. Sin, according to Bittinger, is self-centered behavior, rather than
\nGod-centered behavior. All sin, according to him, ultimately comes down to acting in selfish, self-centered ways. The reason that this is interesting is that I’m not sure if I’ve ever read
\nsuch a totally self-centered, self-obsessed book as this one. This book is a monstrosity of
\nself-indulgence. As a Jew who’s spent time dealing with too many fundamentalist Christians, I’ve got to say that there’s something very fundamentalist christian<\/em> about the idea of writing an
\nincredibly self-centered text about how self-centeredness is the root of all evil; I’ve never seen
\nany group of people so likely to engage in this kind of blatant hypocrisy about self-centeredness. Other people<\/em> are always wrong when they’re doing something self-centered – but when
\nthe Real True Christian<\/em> does it, they’re not being self-centered, they’re just doing what
\nGod wants.<\/p>\n

\n

Then we move on to chapter four, and things get fun, in a pathetic sort of way. How many times have I
\nmocked bad probability arguments? Well, this takes things to a whole new level of badness. Bittinger wants
\nto use a pretty common argument: that Christianity somehow satisfies a set of prophecies, and that the likelihood of those prophecies being satisfied by a random event is vanishingly small. Like I said, a remarkably common argument.<\/p>\n

But Bittinger’s approach to it is the worst I’ve ever seen. He picks nine prophecies
\nthat he claims were satisfied, and slaps together some of the strangest probability numbers
\nfor them that I’ve ever seen. It’s like a compendium of all of the classical mistakes of
\nprobabilistic arguments, all wrapped up in one neat little pile. Back when GM\/BM was on blogger, I
\nproposed my own taxonomy of the fundamental errors of statistical and\/or probabilistic arguments. The basic errors are:<\/p>\n

    \n
  1. Big Numbers: This consists of using our difficulty in really comprehending how huge numbers work to say that beyond a certain probability, things become impossible. So in using this argument, you
    \nuse other tricks to create an incredibly huge number that is allegedly the odds against
    \nsomething happening – and then say “See, it’s too improbable!”.<\/li>\n
  2. Perspective errors: using a priori estimates of something to predict the likelihood of
    \na specific event that actually occurred. Shuffle a deck of cards, and then<\/em> ask
    \nwhat was the likelihood of getting this specific order?<\/li>\n
  3. False independence errors: computing probabilities separately, and then combining them without
    \nconsidering how they interact. Given absolutely no information, what’s the probability that my birthday is in July? 31\/365. Then, separately, what’s the probability that my birthday is in summer? 1\/4. So the probability of my being born during the summer in July is 1\/4×31\/365.<\/li>\n
  4. Fake numbers: generally part of a big numbers argument. You want to inflate the numbers,
    \nso you include as many factors as you can. But some of them are hard (or even impossible)
    \nto figure out. So you just pull numbers out of the air, and throw them into the equation.<\/li>\n
  5. Misshapen search space: Make some event look unlikely by pulling a switch; instead of
    \ncomputing its probability in the real setting in which the event would occur, create a
    \ndifferent setting where it’s more unlikely, and then compute the probability there.<\/li>\n<\/ol>\n

    Bittinger arguably makes every single one of those. In particular, all of his
    \nprophecy arguments are plagued by perspective errors. Every one of them is built by
    \nassuming that Jesus was the messiah, and then going back and computing probabilities of events
    \na priori. So they all require that you first accept that Jesus was the Messiah, and that the prophecies were about him, as priors – but then calculates the probabilities without that
    \nprior. Classic perspective error. But he also makes all of the others (except, arguably,
    \nthe search space, but I think he even does that one!)<\/p>\n

    I’ll show you two examples of how he calculated probabilities for prophecies, and where each category of error occurs in them: the supposed prophesy about the betrayal of Jesus, and the prophecy of
    \nthe virgin birth.<\/p>\n

    For the betrayal, he pulls out an early Jewish text that makes reference to the throwing of 30 pieces of silver; and claims that the betrayal of Jesus in exchange for 30 pieces of silver is a fulfillment of
    \nthat prophecy. Here’s how he puts together a probability for it:<\/p>\n

      \n
    1. Time: The prophet lived 500 years before Jesus. The probability of the predicted event occuring
      \nafter 500 years is, apparently, 1 in 1000. (Fake numbers, perspective.)<\/li>\n
    2. Currency: There were 7 ways of paying for things: silver, gold, bronze, land, crops, animals, or labor.
      \nTherefore the odds of correctly predicting silver were 1 in 7. (Again, fake numbers – why
      \nspecifically choose seven for this? Is including “labor” appropriate? Are all seven equally
      \nprobably? It’s just a way of adding a factor of seven for no particularly good reason.)<\/li>\n
    3. Delivery: The money was thrown down at the betrayer; there are two ways of presenting money: placing it, or throwing it. So the odds of predicting “throwing” are 1 in 2. (Once again – fake numbers. Strangely, this seems like he’s missing lots of opportunities. Why are the only options “place” or “throw”? You could hand it, place it, drop it, throw it, etc.)<\/li>\n
    4. Location: the money was thrown down either inside or outside the temple – so again, 1 in 2. (Fake numbers\/Misshapen space, but in a strange way. Why only those two choices? There are thousands of places
      \nthat the money could have been delivered. But why restrict it this way?)<\/li>\n
    5. Quantity: his estimates that the payment could be between 1 and 1000 pieces of silver, and
      \nend up deciding, without explanation, that the correct balance sets the probability of 30
      \npieces of silver as 1 in 100. (Fake numbers, and false independence. The probability of the payoff being “30 pieces of silver” is not<\/em> independent of the probability of the payoff being silver coins.)<\/li>\n
    6. … and so on<\/li>\n<\/ol>\n

      Numbers randomly drawn out of a hat, slapped together arbitrarily, assuming the desired conclusion,
      \nignoring issues of independence, in order to reach something that supports the desired conclusion.
      \nCan anyone<\/em>, Christian or not, actually look at that train wreck of a probability estimate, and claim that it should be taken seriously? Could anyone actually find that convincing?<\/p>\n

      Well, if you think so, I’ve got news for you: it gets worse, so even if you’re willing to accept
      \nthe dreck above as some kind of valid mathematical argument, you’re going to be surprised by what comes next.<\/p>\n

      What’s the probability of Jesus being the result of a virgin birth? How does he compute a number for that? His first estimate is 1 in 84,850,000. Why? Because that’s an estimate of the number of women of
      \nchildbearing age in the world at the time. And since there was only one virgin birth, the odds of Mary being the one is that number. Then he inflates it more, because, of course, there was only one virgin birth in the history of humanity. So he can use the entire number of women ever<\/em>, and use that as the denominator of the probability. (Big numbers, perspective errors, fake numbers, and misshapen space – all colliding in one great, hideous pileup.)<\/p>\n

      This second example of how he argues about probabilities makes his tactics and his errors extra clear.
      \nFirst of all, there’s the fact that the numbers are, pretty much, pulled out of his ass. But more
      \nimportantly, they measure the wrong things<\/em>. They combine perspective errors and bad space errors, by claiming to be computing the probability of one thing, when in fact, they’re using fake numbers<\/em> to compute the probability of some entirely different thing<\/em>. When someone wants to know what’s the probability of something like the supposed virgin birth, they’re not asking: “if you assume that there was<\/em> a virgin birth and that Mary was the mother, what are the a priori odds
      \nthat Mary would have been the mother?”. They’re asking: “what is the probability of something as
      \nout of the ordinary or inexplicable as a virgin birth?”. But Bittinger, through this section, assumes
      \nthe truth of his religious beliefs, and calculates probabilities of irrelevant things, as if they somehow
      \nmean something.<\/p>\n

      It’s really, frankly, just bizarre. Bittinger is a smart guy, not a moron. How can he make arguments this nonsensical, and think that they’re going to be convincing? How can he put his reputation as
      \na mathematician on the line for garbage like this? What in hell was he thinking?<\/p>\n","protected":false},"excerpt":{"rendered":"

      The bulk of this part of the review is looking at the total train-wreck that is chapter 4, which contains Bittinger’s version of dreadful probabilistic arguments for why Christianity must be true. But before I do that, I need to take care of one loose end from part 1. I should have included chapter three […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[6],"tags":[],"class_list":["post-534","post","type-post","status-publish","format-standard","hentry","category-bad-probability"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p4lzZS-8C","jetpack_sharing_enabled":true,"jetpack_likes_enabled":true,"_links":{"self":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/534","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/comments?post=534"}],"version-history":[{"count":0,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/534\/revisions"}],"wp:attachment":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/media?parent=534"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/categories?post=534"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/tags?post=534"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}