Darwinism Cannot Explain The Peacock

In biology evolution means merely change and most textbooks describe biological evolution as a change in the frequency of alleles from generation to generation. However, on debate boards like these, "evolution" usually means some form of neo-Darwinism, to wit, the claim that simple life originated somehow in the past, that all living creatures share a common ancestor, and that this process has been shaped by descent, modification, and natural selection over billions of years.Natural selection is normally held up as an obvious truth. "Imagine," the proponents say, "an animal that is born without eyes while all others of its kind can see. Surely you can see that the chances of this animal surviving long enough to pass its genes on are greatly reduced." After sage nods all around, natural selection is enshrined as the guiding pillar of evolution.Nevertheless natural selection has its share of challenging themes. The peacock's tail, for example, is a puzzling situation. Surely this tail does not enable the peacock to trap food better or to evade predators more easily. What good is it? How could such a bizarre trait have evolved over countless generations of feral cats, raccoons, and the occassional tiger making its owner into lunch? The answer, we are told, is sexual selection. A standard pro-evolution explanation can be found at Page Not Found | dB Skeptic which theorizes that an ornate train proves that the peacock is healthy, virile, and a good genetic contributor for the females in question.However, a seven-year study of peacock mating behavior can be summed up by its title: Peahens do not prefer peacocks with more elaborate trains (Just a moment... ). The authors of the study attempted to pin down exactly what it was about the trains that peahens went for. Was it length, symmetry, number of eye spots, or what? The answer is simple: None of the above. The theory of peacock sexual selection has been falsified.This result has surprised many especially in light of a previous study by Marion Petrie in which she (supposedly) discovered that peahens do indeed prefer males with more eyespots. The appropriate way to express the new findings is simple: Marion Petrie's results could not be replicated.This should not surprise us. Hard numbers are available for medical research indicating that the vast majority of published studies cannot be replicated (Is medical science built on shaky foundations? | New Scientist ) despite concerted effort on the part of highly interested and motivated companies anxious to bring new life-saving treatments to market. This "open secret" is the reason why Ioannidis' article entitled "Why Most Published Research Findings Are False" (http://www.plosmedicine.org/article/infooi/10.1371/journal.pmed.0020124 ) is the most-downloaded technical paper from the journal.Ioannidis pulls no punches in his paper which boldly states, "It can be [proved] that most claimed research findings are false." What exactly is the problem? The first problem is simple: Science is based on a logical fallacy that has been papered over by Bayesian statistics. Going to the heart of the matter he states, "...[this] is a consequence of the convenient, yet ill-founded strategy of claiming conclusive research findings solely on the basis of a single study assessed by formal statistical significance, typically for a p-value less than 0.05."The second problem is bias. Whether we are talking about researchers looking for and finding what they want to find and ignoring findings that don't confirm their beliefs (confirmation bias), researchers dropping subjects from the study in order to obtain that tantalizingly close 95 percent statistical confidence interval (selection bias), or when a publisher chooses to publish a study that shows a relationship and not to publish a study that shows no relationship (publication bias) the results are the same. Ioannidis writes, "Claimed research findings may often be simply accurate measures of the prevailing bias."How much bias is there in evolutionary biology research? Only time will tell.

I was somewhat surprised at the number of responses. I'm quite active on another debate forum and I typically receive only one response a week. With due apologies to everyone that has contributed, obviously I cannot answer everyone. I have selected your post, Blue Jay, since it seemed to be the most complete and thoughtful one.I noted in your post the claim: "The second paper shows that ornamentation does indeed have a positive impact on reproductive fitness, but with considerably less precision than was previously thought. So, fine-scale variation in ornamentation does not influence female decision, but large-scale variation does."This result is really not surprising. The term to describe this is "regression to the mean." A simple look at The Truth Wears Off | The New Yorker shows a very similar situation. Danish zoologist Anders Mller finds that female barn swallows were far more likely to mate with male birds that had long, symmetrical feathers. Since symmetrical features implies "good" genes everyone was excited. It was easily measured, widely applicable, and females seemed to gravitate it. Evolutionary biologists swooned with delight, but on page 3 the problems start. Despite being validated 9 times over the next 3 years it became more and more difficult to validate the theory. As we read in the article: "In 1994, there were fourteen published tests of symmetry and sexual selection, and only eight found a correlation. In 1995, there were eight papers on the subject, and only four got a positive result. By 1998, when there were twelve additional investigations of fluctuating asymmetry, only a third of them confirmed the theory. Worse still, even the studies that yielded some positive result showed a steadily declining effect size. Between 1992 and 1997, the average effect size shrank by eighty per cent."This is a surprisingly similar situation to the paper you quoted, isn't it? The study is "replicated" (sort of) but with a smaller effect. Why does this happen? The article mentions publication bias. The article continues: Leigh Simmons, a biologist at the University of Western Australia, suggested one explanation when he told me about his initial enthusiasm for the theory: I was really excited by fluctuating asymmetry. The early studies made the effect look very robust. He decided to conduct a few experiments of his own, investigating symmetry in male horned beetles. Unfortunately, I couldn’t find the effect, he said. But the worst part was that when I submitted these null results I had difficulty getting them published. The journals only wanted confirming data.-----------------------------Personally I feel that this example is very relevant because it is about what we are talking about: Sexual selection and its supposed confirmation. Sure it gets confirmed - because of selection, reporting, and publishing bias. So what does that prove except that scientists are very good at finding the results they want/need to find? Little by little other studies that confirm it but with lesser and lesser strength will appear until the purported effect will only exist as a myth in the minds of militant evolutionary apologists in debate forums such as this one.Natural selection is, at best, a tautology. Anyone can state that the fittest will survive as long as you can define what "fit" means after you know who does and does not survive. Similarly there is no shortage of ad hoc hypotheses used to explain away the effects of this study. It is possible, some claim, that long ago females really did prefer more ornate tails but evolution has moved on and the train is a trait that will fade away over the next million years of natural selection. It's a great theory, from a certain point of view, as it explains everything and cannot be refuted without waiting a million years.Of course with ad hoc hypotheses you can also explain away failures in astrology, clairvoyance, ESP, divination, and other pseudoscientific theories that have less prestige than Darwinism does. Your last paragraph is a case in point, where you seem to indicate that I must either provide some alternative theory of peacocks or find myself beholden to support the prevailing scientific fad of our times. Sorry, but I'm not afraid to say, "I don't know."I must say, though, I admire your faith that somehow somewhere science will complete the story. It's touching in its naive simplicity.

I had wanted to respond to someone else because I don't want this debate to turn into a purely Arriba vs. Blue Jay, but after looking at the other responses (if I can call them that) it became clear that I needed to respond to you again.Rather than imagining the situation let's take a possible scenario and see how it works out. Let's assume, for example, that you take a standard rapid EIA test (saliva) for HIV and you come back positive. As you can see at Division of Infectious Diseases & Immunology | NYU Langone Health this test is 99.5 percent accurate. What are the chances that a fine monogamous guy like you really is HIV positive? Is it 99.5 percent? How can we calculate the probabilities?The answer is simple: We don't have enough information in the above paragraph to calculate the chance that you really are HIV+ because we need to know your a priori probability of being HIV+ before we can make the calculation. A simple look at Data | Be in the KNOW reveals that there are 1.2 million people in the US that are HIV+ and 20% of those don't know it. So a quick glance at the US population clock shows that there are 315,238,000 people in the US. Accordingly we can calculate that there are 240,000 people in the US who are HIV+ and don't know it and 314,038,000 people in the US who are not HIV positive. As such we can calculate your a priori chance of being HIV+ as 0.076 percent (that is 240,000 / (240,000+315,238,000)).So now if we take a group of 20,000 people there will be (on average) 15 people in that group that are HIV+ and 19,985 who are negative. So if we test all of them using this test, which is 99.5 percent effective, we will get 15 people testing positive for HIV (and really being positive) and 999 people who falsely test positive. Accordingly any person who tests positive for HIV using the rapid EIA saliva test their real chance of actually being HIV+ is (drumroll) 1.48 percent. That means that 98.52 percent of people who test positive for HIV using this test really don't have the disease - and this is with a test that is 99.5 percent accurate!So my message to any other users in this forum who might have suggested that I had "[constructed] a rather elaborate fantasy to explain how the data we have might support the sexual selection hypothesis, and the hypothesis might still be wrong..." is simple: Take a remedial math class and get back to me.To Blue Jay I say this: Don't think that just tightening the p value to 0.01 will be a panacea to the problems that plague science. We know that science is based on the logical fallacy called "affirming the consequent." Accordingly no positive find can ever be certain. You may go out and find a million black ravens, but that doesn't rule out the possibility of a white one that you just haven't found yet. Perhaps we can calculate the subjective probability that all ravens are black using Bayesian statistics, but that will never reach 100 percent.So now that we have taken a look at the math let's return to the peacock train situation. Anyone with a brain can look at the peacock and seriously doubt that the theory of natural selection can actually explain that train that thing away. Maybe it can, but odds are that it can't. In order to use Bayesian statistics we must first know the a priori chance and since we don't know that we are required to make a guess. Yes, for those of you who think that science is some sort of infallible method, scientists spend most of their time guessing. Sorry to burst your bubble.So let's start with a 1 percent chance that sexual selection can explain away the peacock tail. With that in mind we start doing experiments and we find the Marion Petrie study that I had previously mentioned. While I don't have the study at hand (and haven't found it using Google) I think it's fairly safe to say that she must have hit a 95 percent statistical confidence interval or she wouldn't have gotten published. Those of you who paid attention in statistics class might well remember that the probability of the hypothesis (given the evidence) aka P(H|E) can be calculated thusly:P(H|E) = (P(E|H) * P(H)) / P(E)
where the P(E) is P(E|H^true)*P(H)+P(E|H^false)*(1-P(H))
And if you don't believe that you can look at http://www.cs.washington.edu/...urses/cse473/98au/Nov16.html and see that I'm right.Since we are assuming p=0.05 we know that the probability of the evidence (if the hypothesis is true) is 95 percent whereas the probability of the evidence (if the hypothesis is false) is 5 percent so now it's math time!P(E) = 0.95*.01+.05*.99 = .118
So P(H|E) = .01 / .118 = 8.47 percent so after reading about Marion Petrie I must say that although I am now willing to seriously entertain the possibility that natural selection can account for the peacock's train I remain quite sceptical (91.53 percent sceptical, to be exact). So you can imagine what a seven-year study that couldn't replicate Marion Petrie's study does to my confidence in the highly improbable pro-Darwin claims - and that's not even taking bias into account.Additionally for those who think that I should wait to see if that seven-year study can be replicated, I'd like to point out that this study came out in 2008 so even if someone started right away to try to replicate it, we won't have those results until at least 2015.However, should you have other information or calculations, please feel free to share them! I have an open mind.------------------On other issues, I must say that I am really surprised at your reaction. I have posted links to a mathematical proof and numerous non-replicated studies that clearly show that science doesn't work nearly as well as its proponents claim. Yet your response is simply that I must sign on for more of the same! Imagine, for a moment, that some Christian quoted you a verse from the Bible which you found particularly unhelpful. After informing him that you thought the Bible was nearly worthless he then thrust the book into your hand and said, "All right, then, what verse do you recommend?" Wouldn't you stare at him rather dumbfoundedly? If so then surely you can understand my flabbergasted look at you when you suggest that I simply must continue to use a known-flawed method to try to find the truth when a simple look at its results reveal that flipping a coin is much more likely to lead me to the right result than relying on published scientific studies.Praytell, Blue Jay, how would you respond to a witchdoctor after informing him that scaring the evil spirits out of people has a bad track record of curing people and he retorted, "...you provide no alternative ideas that are demonstrably better."Sure maybe I don't have the cure for diabetes in my back pocket, but that doesn't mean I'm going to turn to scaring the evil spirits out of sufferers or using an egg to draw out the negative humours given them by the evil eye. Why should I let some witchdoctor bully me into using methods known to be useless?Let me throw the ball back in your court: What makes you think that truth exists? What makes you think that science can discover truth? What makes you think that your brain, which you believed evolved into the form it is in order to enable you to shag more women, is even capable of recognizing or comprehending truth?Researchers seeking to alleviate their ignorance - nonsense! They're only interested in getting funding, ideally federally funding, and that funding is intended to aid them in procreating as best as they can in order to further spread their genes. Truth has nothing to do with it so stop pretending. Science is how people who can't kick the ball through the goalposts try to get laid.

I can't count the number of times I've heard that science has put a man on the moon and how the Internet wouldn't be possible without science. People roll their eyes and say how hard they laugh that someone uses their computer to say that science doesn't work.On the other hand, I can't count the number of times I've heard that the Earth wouldn't exist without Jesus Christ, much less the Sun, and my body, and there would be no plants or animals to eat. They say that they laugh every time someone stands on the Earth and says they don't believe in God.Although you may find it strange, I don't see any difference between their position and yours. You are two sides of the same coin - a bunch of gibbering idiots insisting that the truths printed in your holy books are beyond questioning.Egyptians build the pyramids, so I guess that means I need to believe in Ra, too, don't I?Here's a news flash for you - the most important reasons I have the computer I'm typing on are three:1. Double-entry accounting.
2. Charles Babbage, mathematician, and his differential engine.
3. Six Sigma Statistical methods.Or are you one of those people who thinks that math is science?

Deja que los perros ladran - es seal que avanzamos."It can be proven that most claimed research findings are false," so says Ioannidis and continues, "...that the high rate of nonreplication (lack of confirmation) of research discoveries is a consequence of the convenient, yet ill-founded strategy of claiming conclusive research findings solely on the basis of a single study assessed by formal statistical significance, typically for a p-value less than 0.05." Why is that? Because the pre-study probability is normally quite low. His example:Let us assume that a team of investigators performs a whole genome association study to test whether any of 100,000 gene polymorphisms are associated with susceptibility to schizophrenia. Based on what we know about the extent of heritability of the disease, it is reasonable to expect that probably around ten gene polymorphisms among those tested would be truly associated with schizophrenia, with relatively similar odds ratios around 1.3 for the ten or so polymorphisms and with a fairly similar power to identify any of them. Then R = 10/100,000 = 10⁻⁴, and the pre-study probability for any polymorphism to be associated with schizophrenia is also R/(R + 1) = 10⁻⁴. Let us also suppose that the study has 60% power to find an association with an odds ratio of 1.3 at α = 0.05. Then it can be estimated that if a statistically significant association is found with the p-value barely crossing the 0.05 threshold, the post-study probability that this is true increases about 12-fold compared with the pre-study probability, but it is still only 12 10⁻⁴.Now let us suppose that the investigators manipulate their design, analyses, and reporting so as to make more relationships cross the p = 0.05 threshold even though this would not have been crossed with a perfectly adhered to design and analysis and with perfect comprehensive reporting of the results, strictly according to the original study plan. Such manipulation could be done, for example, with serendipitous inclusion or exclusion of certain patients or controls, post hoc subgroup analyses, investigation of genetic contrasts that were not originally specified, changes in the disease or control definitions, and various combinations of selective or distorted reporting of the results. Commercially available data mining packages actually are proud of their ability to yield statistically significant results through data dredging. In the presence of bias with u = 0.10, the post-study probability that a research finding is true is only 4.4 10⁻⁴. Furthermore, even in the absence of any bias, when ten independent research teams perform similar experiments around the world, if one of them finds a formally statistically significant association, the probability that the research finding is true is only 1.5 10⁻⁴, hardly any higher than the probability we had before any of this extensive research was undertaken!------------------------Accordingly his a priori odds are 10/100,000 or 0.0001 but when I start by granting you 0.01 - a number 100 times more generous than the one he uses you whine about it like a little bitch. You also didn't pick up, I note, that in the final calculation I gave you P(H|E) = 0.01 / 0.118 - that is, two figures, when the formula specifically has three variables. What you didn't notice is that I assumed P(E|H) was 1 during the last step, even though I had previously assumed it was only 0.95. This was my way of being generous to you - an extra 5 percent bonus so that no one could say I had been unduly parsimonious in my approach. You also seem to think I should grant you some sort of study odds better than p=0.05 despite the fact that the article I was quoting from uses that number specifically.But we both know you didn't read that article. Why would you when you already know the truth as though it were revealed to you from on high? Your real goal is to stamp out the heresy you see before you. Like the rest of your fellow cult members, you have a mind like concrete: All mixed up and permanently set.Really, though, my analysis was even more generous than the above makes out. As you would know if you had bothered to Google Petrie's study, her study design was simple. She found a group of peacocks, captured half of the males, and disfigured portions of their tail. Later she lurked in the bushes watching their copulation and drew her conclusions. Let's analyze this simple study design for the possible reasons why it worked:Reason 1. Maybe she's right. Maybe peahens really do like cocks with lots of eyes.
Reason 2. She was biased and consciously or subconsciously created the result she sought.
Reason 3. The peacocks that had been mangled were traumatized and spent less time seeking out hens and more time hiding - especially when they saw their former assailant lurking in the bushes.
Reason 4. The peahens didn't care about the number of tail eyes, per se but discriminated against the mutilated cocks because of the mulilation alone.
Reason 5. The peahens had grown accustomed to the males having a certain number of eyes in their tails due to long associations whereas had they grown up around males with lower numbers of eyes they would have been accustomed to that and would have rejected males with "excessive" numbers of eyes as out of the norm.Perhaps there are even more reasons that I just can't think of right now. Be that as it may a simple application of the principle of insufficient reason suggests that we assign a 20 percent probability to her claim in light of her study. Even if we assume that her study being replicated ruled out reason 2 (something I am prepared to dispute) we might be able to get the chance up to 0.25 but not more.And that's before the study I previously mentioned that found no relationship. Dare I suggest that those numbers will decline sharply?As for your claim that "The figure you've used for P(H) is merely a quantification of your prejudices..." my response is simple: Se cree ladrn que todos son de su condicin.

It's not that tautologies are often true - tautologies are always true they're just not very useful."Wherever you go, there you are." No one can say that's not true - it's just not a helpful observation.

You greatly mistake the situation. If you had bothered to read the post (or been capable of understanding it) you would realize that we are saying that only one person took the exam and got a positive result. We then took a calculator and worked out the likelihood that this positive result was false and got a number well over 90 percent. Then we concluded that a test that was 99.5 percent accurate was not a panacea to all problems involving false positives.This situation is comparable to published studies because published studies are almost always studies in which a positive relationship is claimed to have been found.Additionally, of course, there's no way to know that the test is 99.5 percent accurate. All we can say for sure is that the test agrees with the Western Blot test 99.5 percent of the time.

It never ceases to amaze me the bad examples that people come up with in fora like these.We got to the moon, you claim, because of science. I suppose you're going to say that it's because of Newton's Law of Gravity that we got there.You ignore, of course, the fact that the Law of Gravity has been falsified. It is not merely the fact that it couldn't predict the precession of Mercury, or that the gravitational lensing observed during the eclipse of 1919 validated Einstein's theories far more than Newton's, or that quantum mechanics has also similarly abandoned the law in favor of gravitons, but also the point that the Law of Gravity cannot explain the movements of galaxies without resorting to a liberal sprinkling of "dark matter" at conveniently located places in order to make the numbers work.No, more to the point I want to point out this: Sir Isaac Newton was a mathematician, alchemist, philosopher, and theologian. Yet when he formulates a (wrong) law science gets the credit. Why is that? Why not math? Why not alchemy? Why not philosophy? Why not theology? Why should science get the credit when Newton wasn't a scientist?P.S. Georg Ohm, the inventor of Ohm's law, was a high school teacher and mathematician. He was fooling around with an electrostatic generator, which was first invented by Johan Wilcke, whose father was a pastor in the German Church in Stockholm. He became a Thamian lecturer and similarly was not a scientist. Again I ask: Why does science get the credit? Why not math? Why not the German Church in Stockholm? In fact, history tells us that scientists called Ohm's Law "a tissue of naked fantasy." Perhaps it would be more appropriate to say I have the computer in spite of science and not because of it.

You state: "But Ioannidis had a reason for producing his figure --- that the results in his hypothetical example were based on data-mining." This only proves that you have no reading comprehension ability.Let's take a simple example of a person who is doing a test on 100 chemicals believed to treat some disease. Only 1 of those chemicals is actually effective. He will find that 1 effective treatment 95 percent of the time and will find, on average, 5 of the ineffective treatments to also be effective. That's because 1 study out of 20 will reach a 95 percent confidence interval by chance alone. So right out of the gate we have 83 percent of his published work as wrong. This has nothing to do with data mining.Imagine, however, that unknown to the first researcher there is a second researcher who is also studying the same 100 chemicals. He will also find the 1 workable treatment and 5 ineffective treatments. It is unlikely that they will coincide on any of the false positives. Accordingly we will see 10 false positives and 1 true positive assuming that all of these results get published.Going further, what would happen if one of the researchers decided to double test each compound? Do you think such a thing couldn't happen? A simple look at Page Not Found | Reuters reveals this tidbit: "We went through the paper line by line, figure by figure," said Begley. "I explained that we re-did their experiment 50 times and never got their result. He said they'd done it six times and got this result once, but put it in the paper because it made the best story. It's very disillusioning." Yes, that's right, some researchers do the same experiment multiple times and only report the one time that it works. That's what we call selection bias. Believe me, it happens.In this case the double-testing researcher will then show up with 10 false positives, one of which will coincide with one of the 5 false positives of the other researcher. So we will have:1 true positive found by both
1 false positive found by both
4 false positives found by researcher 1
9 false positives found by researcher 2So that means that even if we only take those positives found by both researchers that 50 percent of the time it will be a false positive. This doesn't include bias, bad study design, or data mining.This is not brain surgery. It's simple math - something you're supposed to be good at.

You're missing the point.Imagine that you are feeling sick and you go to a doctor. The doctor draws some blood and you see him again in a few days. He says, "We ran a bunch of tests, but everything came back negative." So you go to another doctor. He tells you the same: that he'd run some tests, and they all came back negative. The third doctor, however, tells you that you've tested positive for HIV. Now it's entirely possible that the other doctors also did a standard test for HIV and you came back negative twice but they didn't tell you. They just said they'd run some tests and they'd all come back negative. You didn't get an in-depth report on exactly what tests had been run because you didn't think that negative results were important. Well, they are important - very important.

I can tell you for a fact that Newton wasn't a scientist. All you need to do is pick up a dictionary (try dictionary.com) and you'll see that the word "scientist" got added to the dictionary in 1834. Since Sir Isaac Newton died in 1727 we know that he was not a scientist. Those people who claim that he was are simply trying to revise history in such a way as to promote their own prejudices.Some people, for example, will argue that before they were called scientists they were called "natural philosophers" pointing to things like Sir Isaac's publication of The Mathematical Principles of Natural Philosophy. Be aware, however, that if you take that claim you are excluding Galileo as a scientist as he wasn't a natural philosopher. In fact, those people who opposed him most bitterly and refused to look through his telescope because it "gave them a headaches" were the natural philosophers of their time.Galileo was a devout Catholic and medical school dropout turned amateur mathematician and astronomer. All of his discoveries were verified by Jesuit priests. Perhaps Catholicism is more responsible for us getting to the moon than science, eh?

According to evolutionary theory once, a very long time ago, peacocks had normal, small tails. However, by chance, a certain peacock had a larger, more ornate tail and the peahens just creamed their little non-existent panties for him and he had greater reproductive success. Over time and further similar incidents the peacock tail has evolved into what it is nowadays. This belief has been widely preached among the evolutionist faithful since 1859.However, it had never been scientifically tested. Why bother? Faith should be enough for the acolytes, shouldn't it?Anyway in the 1980s someone finally decided to "test" it. First of all, Petrie took groups of feral peacocks and captured them in order to trim their tails. The control group was not captured. Accordingly she observed that peacocks with mutilated tails had lesser chances of success.In 2008 Japanese ecologists observed feral peacocks without plucking their tails and found no relationship between tails and reproductive success - rather they found that peacocks who had deeper, throatier voices, were more adept at seducing the hens.Dakin, whose own studies were underway in 2008 when the Japanese study came out, noted that males had the same success regardless of eyespots (which varied between 165 and 170) but plucked males had markedly lower success.From Size doesn't always matter for peacocks | Nature : Petrie admits that traits such as the number of eyespots are only rough measures of tail quality, and probably mean more to scientists than to peahens. "At the end of the day, we will never know what peahens are looking at and how they select their mates. You can't ask them."So what do we really know? Only that plucked males have less success for some reason. This is nothing more than bad study design.A proper study would have four groups.1. Uncaptured, unaffected males (the control group).
2. Captured males who experienced several close passes of the electric razor (complete with sound) but whose tails were not altered in any way (the traumatized group).
3. Captured males whose eyespots were shaved out.
4. Captured males whose tails were shaved without affecting eyespots.The peacock groups should be randomized and the researchers observing the copulations should not know which group each male they are observing fits into (hard to do with those whose spots have been butchered, but at least they should go through the motions).My additional questions: How were the males captured? Did researchers simply resolve to capture half of the males and then mutilate their tails? Couldn't that imply that the males who got mutilated were the ones who were the easiest to capture? Or had they specifically set out to capture certain pre-selected males? We may never know since the study in question is paywalled.In my opinion, the study is chintzy. I also believe that Petrie's pathological need to publish a rebuttal to the Japanese study (also paywalled) shows that she is heavily emotionally invested in the outcome - a bad sign.

I think you are solidly missing the point.We have a test (let’s forget about HIV for a second) that tests for a disease. Let’s call it test A.We know this test is 99.5 percent accurate because we’ve compared it against another test (let’s call it test B) and it agrees with it 99.5 percent of the time in controlled clinical trials.But how do we know test B is accurate? I suppose you tested that test against test C, which was in turn validated against test D, and that was validated against test E, ad infinitum.That’s what we call an infinite regress.Nor does your logic eliminate the possibility of false positives being reported. We can imagine a researcher who is investigating some 300 medications to see which ones are effective against a certain ailment. In reality none of them are effective. Yet he will still find that, on average, 15 will reach a 95 percent confidence level on by chance alone. If some or all of those get published then he will have a method that is 95 percent effective yet published results that are 100 percent wrong.

You said, If it is deleterious it will be selected against.Let’s talk about guinea pigs. As you may or may not know, guinea pigs used to synthesize vitamin C. Now they no longer do. In fact they must receive vitamin C supplementation or they will experience rough hair coat, lack of appetite, dental pain, delayed wound healing, lameness, and an inability to fend off infections.Now according to your faith in neo-Darwinism this must somehow not be deleterious because it is universal in the gene pool. I should very much like to hear your explanation as to why that is.

Ok, as you’ve said, Einstein was a patent clerk. Yet you decided to give the credit for his invention to science, as opposed to Judaism, or Deism, or the patent office. You have made no justification for this. Why should I accept it?