Monthly Archives: September 2006

Pathetic Statistics from HIV/AIDS Denialists

While I was on vacation, I got some email from Chris Noble pointing me towards a discussion with some thoroughly innumerate HIV-AIDS denialists. It’s really quite shocking what passes for a reasonable argument among true believers.
The initial stupid statement is from one of Duesberg’s papers, [AIDS Acquired by Drug Consumption and Other Noncontagious Risk Factors][duesberg], and it’s quite a whopper. During a discussion of the infection rates shown by HIV tests of military recruits, he says:
>(a) “AIDS tests” from applicants to the U.S. Army and the U.S. Job
>Corps indicate that between 0.03% (Burke et al.,1990) and 0.3% (St
>Louis et al.,1991) of the 17- to 19-year-old applicants are HIV-infected
>but healthy. Since there are about 90 million Americans under the age
>of 20, there must be between 27,000 and 270,000(0.03%-0.3% of 90
>million) HIV carriers. In Central Africa there are even more, since 1-2%
>of healthy children are HIV-positive (Quinn et al.,1986).
>
>Most, if not all, of these adolescents must have acquired HIV from
>perinatal infection for the following reasons: sexual transmission of
>HIV depends on an average of 1000 sexual contacts, and only 1in 250
>Americans carries HIV (Table 1). Thus, all positive teenagers would
>have had to achieve an absurd 1000 contacts with a positive partner, or
>an even more absurd 250,000 sexual contacts with random Americans
>to acquire HIV by sexual transmission. It follows that probably all of
>the healthy adolescent HIV carriers were perinatally infected, as for
>example the 22-year-old Kimberly Bergalis (Section 3.5.16).
Now, I would think that *anyone* who reads an allegedly scientific paper like this would be capable of seeing the spectacular stupidity in this quotation. But for the sake of pedantry, I’ll explain it using small words.
If the odds of, say, winning the lottery are 1 in 1 million, that does *not* mean that if I won the lottery, that means I must have played it one million times. Nor does it mean that the average lottery winner played the lottery one million times. It means that out of every one million times *anyone* plays the lottery, *one* person will be expected to win.
To jump that back to Duesberg, what he’s saying is: if the transmission rate of HIV/AIDS is 1 in 1000, then the average infected person would need to have had sex with an infected partner 1000 times.
Nope, that’s not how math works. Not even close.
Suppose we have 1000 people who are infected with HIV, and who are having unprotected sex. *If* we follow Duesberg’s lead, and assume that the transmission rate is a constant 0.1%, then what we would expect is that if each of those 1000 people had sex with one partner one time, we would see one new infected individual – and that individual would have had unprotected sex with the infected partner only one time.
This isn’t rocket science folks. This is damned simple, high-school level statistics.
Where things get even sadder is looking at the discussion that followed when Chris posted something similar to the above explanation. Some of the ridiculous contortions that people go through in order to avoid admitting that the great Peter Duesberg said something stupid is just astounding. For example, consider [this][truthseeker] from a poster calling himself “Truthseeker”:
>If Duesberg had said that, he would indeed be foolish. The foolishness,
>however, is yours, since you misintepret his reasoning. He said, as you note
>
>>Most, if not all, of these adolescents must have acquired HIV from perinatal
>>infection for the following reasons: sexual transmission of HIV depends on an
>>average of 1000 sexual contacts, and only 1 in 250 Americans carries HIV
>>(Table 1). Thus, all positive teenagers would have had to achieve an absurd
>>1000 contacts with a positive partner, or an even more absurd 250,000 sexual
>>contacts with random Americans to acquire HIV by sexual transmission.
>
>This states the average transmission requires 1000 contacts, not every
>transmission. With such a low transmission rate and with so few Americans
>positive – you have to engage with 250 partners on average to get an average
>certainty of 100% for transmission, if the transmission rate was 1. Since it is
>1 in 1000, the number you have to get through on average is 250,000. Some might
>do it immediately, some might fail entirely even at 250,000. But the average
>indicates that all positive teenagers would have had to get through on average
>250,000 partner-bouts.
Truthseeker is making exactly the same mistake as Duesberg. The difference is that he’s just had it explained to him using a simple metaphor, and he’s trying to spin a way around the fact that *Duesberg screwed up*.
But it gets even worse. A poster named Claus responded with [this][claus] indignant response to Chris’s use of a metaphor about plane crashes:
>CN,
>
>You would fare so much better if you could just stay with the science
>points and refrain from your ad Duesbergs for more than 2 sentences at
>a time. You know there’s a proverb where I come from that says ‘thief thinks
>every man steals’. I’ve never seen anybody persisting the way you do in
>calling other people ‘liars’, ‘dishonest’ and the likes in spite of the
>fact that the only one shown to be repeatedly and wilfully dishonest
>here is you.
>
>Unlike yourself Duesberg doesn’t deal with matters on a case-by-case only basis
>in order to illustrate his statistical points. precisely as TS says, this shows
>that you’re the one who’s not doing the statistics, only the misleading.
>
>In statistics, for an illustration to have any meaning, one must assume that
>it’s representative of an in the context significant statistical average no?
>Or perphaps in CN’s estimed opinion statistics is all about that once in a
>while when somebody does win in the lottery?
Gotta interject here… Yeah, statistics *is* about that once in a while when someone wins the lottery, or when someone catches HIV, or when someone dies in a plane crash. It’s about measuring things by looking at aggregate numbers for a population. *Any* unlikely event follows the same pattern, whether it’s catching HIV, winning the lottery, or dying in a plane crash, and that’s one of the things that statistics is specifically designed to talk about: that fundamental probabilistic pattern.
>But never mind we’ll let CN have the point; the case in question was that odd
>one out, and Duesberg was guilty of the gambler’s fallacy. ok? You scored one
>on Duesberg, happy now? Good. So here’s the real statistical point abstracted,
>if you will, from the whole that’s made up by all single cases, then applied to
>the single case in question:
>
>>Thus, all positive teenagers would have had to achieve an absurd 1000 contacts
>>with a positive partner, or an even more absurd 250,000 sexual contacts with
>>random Americans to acquire HIV by sexual transmission.
>
>This is the statistical truth, which is what everybody but CN is interested in.
Nope, this is *not* statistical truth. This is an elementary statistical error which even a moron should be able to recognize.
>Reminder: Whenever somebody shows a pattern of pedantically reverting to single
>cases and/or persons, insisting on interpreting them out of all context, it’s
>because they want to divert your attention from real issues and blind you to
>the overall picture.
Reminder: whenever someone shows a pattern of pedantically reverting to a single statistic, insisting on interpreting it in an entirely invalid context, it’s because they want to divert your attention from real issues and blind you to the overall picture.
The 250,000 average sexual contacts is a classic big-numbers thing: it’s so valuable to be able to come up with an absurd number that people will immediately reject, and assign it to your opponents argument. They *can’t* let this go, no matter how stupid it is, no matter how obviously wrong. Because it’s so important to them to be able to say “According to *their own statistics*, the HIV believers are saying that the average teenage army recruit has had sex 250,000 times!”. As long as they can keep up the *pretense* of a debate around the validity of that statistic, they can keep on using it. So no matter how stupid, they’ll keep defending the line.
[duesberg]: www.duesberg.com/papers/1992%20HIVAIDS.pdf
[truthseeker]: http://www.newaidsreview.org/posts/1155530746.shtml#1487
[claus]: http://www.newaidsreview.org/posts/1155530746.shtml#1496

A Gift for PZ

While I was away on vacation, my family made a stop in Corning NY to see the Corning Glass Museum. I had to snap this photo for PZ. Alas, all I had was the camera in my cellphone, so the resolution leaves something to be desired, but it’s the thought that counts, right?
08-27-06_1108.jpg

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Programs are Proofs: Models and Types in Lambda Calculus

Lambda calculus started off with the simple, untyped lambda calculus that we’ve been talking about so far. But one of the great open questions about lambda calculus was: was it sound? Did it have a valid model?
Church found that it was easy to produce some strange and non-sensical expressions using the simple lambda calculus. In order to try to work around those problems, and end up with a consistent system, Church introduced the concept of *types*, producing the *simply typed lambda calculus*. Once types hit the scene, things really went wild; the type systems for lambda calculi have never stopped developing: people are still finding new things to do by extending the LC type system today! Most lambda calculus based programming languages are based on the Hindley-Milner lambda calculus, which is a simplification of one of the standard sophisticated typed lambda calculi called *SystemF*. There’s even a [Lambda Cube][cube], though it’s not related to the Time Cube. Once people really started to understand types, they realized that the *untyped* lambda calculus was really just a pathologically simple instance of the simply typed lambda calculus: a typed LC with only one base type.
The semantics of lambda calculus are easiest to talk about in a typed version. For now, I’ll talk about the simplest typed LC, known as the *simply typed lambda calculus*. One of the really amazing things about this, which I’ll show, is that a simply typed lambda calculus is completely semantically equivalent to an intuitionistic propositional logic: each type in the program is a proposition in the logic; each β reduction corresponds to an inference step; and each complete function corresponds to a proof! Look below the fold for how.

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