Category Archives: Probability and statistics

The Real EleP(T|H)ant in the Room

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TSZ has made much ado about P(T|H), a conditional probability based on a materialistic hypothesis. They don’t seem to realize that H pertains to their position and that H cannot be had means their position is untestable. The only reason the conditional probability exists in the first place is due to the fact that the claims of evolutionists cannot be directly tested in a lab. If their claims could be directly tested then there wouldn’t be any need for a conditional probability.

If P(T|H) cannot be calculated it is due to the failure of evolutionists to provide H and their failure to find experimental evidence to support their claims.

I know what the complaints are going to be- “It is Dembski’s metric”- but yet it is in relation to your position and it wouldn’t exist if you actually had something that could be scientifically tested.

 

 

Wright, Fisher, and the Weasel

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Richard Dawkins’s computer simulation algorithm explores how long it takes a 28-letter-long phrase to evolve to become the phrase “Methinks it is like a weasel”. The Weasel program has a single example of the phrase which produces a number of offspring, with each letter subject to mutation, where there are 27 possible letters, the 26 letters A-Z and a space. The offspring that is closest to that target replaces the single parent. The purpose of the program is to show that creationist orators who argue that evolutionary biology explains adaptations by “chance” are misleading their audiences. Pure random mutation without any selection would lead to a random sequence of 28-letter phrases. There are 27^{28} possible 28-letter phrases, so it should take about 10^{40} different phrases before we found the target. That is without arranging that the phrase that replaces the parent is the one closest to the target. Once that highly nonrandom condition is imposed, the number of generations to success drops dramatically, from 10^{40} to mere thousands.

Although Dawkins’s Weasel algorithm is a dramatic success at making clear the difference between pure “chance” and selection, it differs from standard evolutionary models. It has only one haploid adult in each generation, and since the offspring that is most fit is always chosen, the strength of selection is in effect infinite. How does this compare to the standard Wright-Fisher model of theoretical population genetics? Continue reading →

Philosophy and Complexity of Rube Goldberg Machines

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Michael Behe is best known for coining the phrase Irreducible Complexity, but I think his likening of biological systems to Rube Goldberg machines is a better way to frame the problem of evolving the black boxes and the other extravagances of the biological world.
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Spontaneous Generation

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A century later we know that the overwhelming obstacle facing spontaneous generation is probability, or rather improbability, resulting from life’s enormously complex phenotypes. If even a single protein, a single specific sequence of amino acids, could not have emerged spontaneously, how much less so could a bacterium like E. coli with millions of proteins and other complex molecules? Modern biochemistry allows us to estimate the odds, and they demolish the spontaneous creation of complex organisms.

Looks like IDists aren’t the only ones to appeal to probability arguments. How does Wagner know what the probabilities are, or that spontaneous generation is even within the realm of what is possible?

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“Uncommon Descent” and “The Skeptical Zone” in 2015

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(crossposted from here and here)

(Edited Feb 2, 2016 to add eight figures)

Since 2005, Uncommon Descent (UD) – founded by William Dembski – has been the place to discuss intelligent design. Unfortunately, the moderation policy has always been one-sided (and quite arbitrary at the same time!) Since 2011, the statement “You don’t have to participate in UD” is not longer answered with gritted teeth only, but with a real alternative: Elizabeth Liddle’s The Skeptical Zone (TSZ). So, how were these two sites doing in 2015?

Number of Comments 2005 – 2015

year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
UD  8,400 23,000 22,400 23,100 41,100 24,800 41,400 28,400 42,500 53,700 53,100
TSZ – – – – – –  2,200 15,100 16,900 20,400 45,200

In 2015, there were still 17% more comments at UD than at TSZ – 53,100 to 45,200.

UD-2015-02

Though UD is still going strong, there is a slight downwards trend (yellow line) in the daily number of comments.
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The Reasonableness of Atheism and Black Swans

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As an ID proponent and creationist, the irony is that at the time in my life where I have the greatest level of faith in ID and creation, it is also the time in my life at some level I wish it were not true. I have concluded if the Christian God is the Intelligent Designer then he also makes the world a miserable place by design, that He has cursed this world because of Adam’s sin. See Malicious Intelligent Design.
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Intention, Intelligence and Teleology

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On the left is a photograph of a real snowflake.  Most people would agree that it was not created intentionally, except possibly in the rather esoteric sense of being the foreseen result of the properties of water atoms in an intentionally designed universe in which water atoms were designed to have those properties.  But I think most people here, ID proponents and ID critics alike, would consider that the “design” (in the sense of “pattern”) of this snowflake is neither random nor teleological.  Nor, however, is it predictable in detail.  Famously “no two snowflakes are alike”, yet all snowflakes have six-fold rotational symmetry.  They are, to put it another way, the products of both “law” (the natural law that governs the crystalisation of water molecules) and “chance” (stochastic variation in humidity and temperature that affect the rate of growth of each arm of the crystal as it grows). We need not, to continue in Dembski’s “Explanatory Filter” framework, infer “Design”.

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Those Weasely IDCists!

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A couple recent comments here at TSZ by Patrick caught my eye.

First was the claim that arguments for the existence of God had been refuted.

I don’t agree that heaping a bunch of poor and refuted arguments together results in a strong argument.

here

Second was the claim that IDCists do not understand cumulative selection and Dawkins’ Weasel program.

The first time I think I was expecting more confusion on Ewert’s part about the power of cumulative selection (most IDCists still don’t understand Dawkin’s Weasel).

here

In this OP I’d like to concentrate on the second of these claims.

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Answer to Barry Part 1 (and, inadvertently, 2)

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Barry seems to have noticed TSZ again, and so I will take this opportunity of inviting him over here, where he can post freely, and will not be banned unless he posts porn or malware or outs someone, which I expect he can manage not to do.

And he responds to my post, Lawyers and Scientists.  He does so in two parts, so I will devote two posts to them.  Here is my response to his first part.  Barry writes:

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More statistical confusion…

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At UD I noticed, while I was checking the Moran-Arrington score, I couldn’t help noticing a news item entitled, provocatively (for me) Psychology does not speak the language of statistics very well.

So being a psychologist who teaches statistical methods to psychology students, I had to click, and found that it was a report of a blog piece here called Statistics Shows Psychology Is Not Science

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Lawyers and Scientists

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There’s been a skirmish between Larry Moran and Barry Arrington about whether Barry understands the Theory of Evolution, and the latest salvo is a piece at UD, entitled, Can a Lowly Lawyer Make a Useful Contribution? Maybe.

Well, in a sense, Barry makes a useful contribution in that post, as he gives a very nice illustration of a common misunderstanding about the process of hypothesis testing, in this case, basic model-fitting and null hypothesis testing, the workhorse (with all its faults) of scientific research.  Barry writes:

[Philip]Johnson is saying that attorneys are trained to detect baloney.  And that training is very helpful in the evolution debate, because that debate is chock-full of faulty logic (especially circular reasoning), abuse of language (especially equivocations), assumptions masquerading as facts, unexamined premises, etc. etc.

Consider, to take one example of many, cladistics.  It does not take a genius to know that cladistic techniques do not establish common descent; rather they assume it.  But I bet if one asked, 9 out of 10 materialist evolutionists, even the trained scientists among them, would tell you that cladistics is powerful evidence for common descent.  As Johnson argues, a lawyer’s training may help him understand when faulty arguments are being made, sometimes even better than those with a far superior grasp of the technical aspects of the field.  This is not to say that common descent is necessarily false; only cladistics does not establish the matter one way or the other.

In summary, I am trained to evaluate arguments by stripping them down to examine the meaning of the terms used, exposing the underlying assumptions, and following the logic (or, as is often the case, exposing the lack of logic).  And I think I do a pretty fair job of that, both in my legal practice and here at UD.

Barry has made two common errors here.  First he has confused the assumption of common descent with the conclusion of common descent, and thus detected circular reasoning where there is none.  Secondly he has confused the process of fitting a model with the broader concept of a hypothesised model.

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Random Genetic Drift: a controversy?

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Over my time as a dilettante observer of the science blogging community, I have noticed a certain frisson of controversy over the idea of random genetic drift. Sewall Wright, who with Ronald Fisher and J. B. S. Haldane (Bill Bryson’s observations on Haldane’s research into diving and decompression are entertaining) established the science of population genetics, is credited with coining the phrase in 1929. Thanks to Professor Joe Felsenstein for pointing out his seminal paper. Continue reading →

CSI-free Explanatory Filter…

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…Gap Highlighter, Design Conjecture

Though I’ve continued to endear myself to the YEC community, I’ve certainly made myself odious in certain ID circles. I’ve often been the lone ID proponent to vociferously protest cumbersome, ill-conceived, ill-advised, confusing and downright wrong claims by some ID proponents. Some of the stuff said by ID proponents is of such poor quality they are practically gifts to Charles Darwin. I teach ID to university science students in extra curricular classes, and some of the stuff floating around in ID internet circles I’d never touch because it would cause my students to impale themselves intellectually.
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Circularity of using CSI to conclude Design?

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At Uncommon Descent, William Dembski’s and Robert Marks’s coauthor Winston Ewert has made a post conceding that using Complex Specified Information to conclude that evolution of an adaptation is improbable is in fact circular. This was argued at UD by “Keith S.” (our own “keiths”) in recent weeks. It was long asserted by various people here, and was argued in posts here by Elizabeth Liddle in her “Belling the Cat” and “EleP(T|H)ant in the room” series of posts (here, here, and here). I had posted at Panda’s Thumb on the same issue.

Here is a bit of what Ewert posted at UD:

CSI and Specified complexity do not help in any way to establish that the evolution of the bacterial flagellum is improbable. Rather, the only way to establish that the bacterial flagellum exhibits CSI is to first show that it was improbable. Any attempt to use CSI to establish the improbability of evolution is deeply fallacious.

I have put up this post so that keiths and others can discuss what Ewert conceded. I urge people to read his post carefully. There are still aspects of it that I am not sure I understand. What for example is the practical distinction between showing that evolution is very improbable and showing that it is impossible? Ewert seems to think that CSI has a role to play there.

Having this concession from Ewert may surprise Denyse O’Leary (“News” at UD) and UD’s head honcho Barry Arrington. Both of them have declared that a big problem for evolution is the observation of CSI. Here is Barry in 2011 (here):

All it would take is even one instance of CSI or IC being observed to arise through chance or mechanical necessity or a combination of the two. Such an observation would blow the ID project out of the water.

Ewert is conceding that one does not first find CSI and then conclude from this that evolution is improbable. Barry and Denyse O’Leary said the opposite — that having observed CSI, one could conclude that evolution was improbable.

The discussion of Ewert’s post at UD is interesting, but maybe we can have some useful discussion here too.

Ronald Fisher and William Dembski

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An odd post by “news” at UD raises yet again the issue of Fisherian p values – and reveals yet again that many ID proponents don’t understand them.

She (I assume it is Denyse) writes:

Further to “Everyone seems to know now that there’s a problem in science research today and “At a British Journal of Medicine blog, a former editor says, medical research is still a scandal,” Ronald Fisher’s p-value measure, a staple of research, is coming under serious scrutiny.

Many will remember Ronald Fisher (1890–1962) as the early twentieth century Darwinian who reconciled Darwinism with Mendelian genetics, hailed by Richard Dawkins as the greatest biologist since Darwin. Hid original idea of p-values (a measure of whether an observed result can be attributed to chance) was reasonable enough, but over time the dead hand got hold of it:

 

Many at UD may also “remember” Ronald Fisher as the early twentieth century statistician who inspired William Dembski’s eleP(T|H)ant.

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Counting generations of M&Ms

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Allan Miller’s post Randomness and evolution deals with neutral drift in the Moran model applied to a bag of M&Ms. Much of the discussion has focused on the question of counting generations in a situation where they overlap. I think it’s a good idea to divert that part of the discussion into its own thread.

Here are the rules. Start with a population of N M&Ms. A randomly chosen M&M dies. Another randomly chosen M&M gives birth to a child M&M. Repeat.

Because the focus of this thread is generation count and not fixation, we will pay no attention to the colors of M&Ms.

How do we count generations of M&Ms?

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Randomness and evolution – An Interactive toy

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Lizzie Allan Miller said:

Here’s a simple experiment one can actually try. Take a bag of M&M’s, and without peeking reach in and grab one. Eat it. Then grab another and return it to the bag with another one, from a separate bag, of the same colour. Give it a shake. I guarantee (and if you tell me how big your bag is I’ll have a bet on how long it’ll take) that your bag will end up containing only one colour. Every time. I can’t tell you which colour it will be, but fixation will happen.

I’ve written an interactive browser based version you can explore this idea with.

http://mandmcounter.appspot.com/emeniem.html

color

 

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