Keep doing that and you'll go blind

“Keep doing that and you’ll go blind”
That’s what our priest warned us about.
•••
Maybe with a big enough grant, a computer model could determine the number of cataracts caused by the 0.7ºC global warming over the past century and a half. The number might be alarming!
More public funding required.

Well, when you put it that way, it seems pretty obvious. Marvelous thinking BTW, but I can’t help wondering how something so obvious gets past so many people to allow this to be anywhere near to being written or published and especially when those people are are supposed experts, in positions of authority or at least educated. No wonder I am leery of the pronouncemenst of ‘experts’ in any field.

Hey, PEER REVIEWED science! And you have the audacity to even question it ??? lol
That’s not far from climate “science” is it? They must be speaking of anomaly temperatures. Wait, are these scientists climate trained?

http://xkcd.com/882/

I think they call it “data dredging.” Funny thing is that just earlier this evening I had almost the SAME argument used against me on some board: a claim that some study showed that childhood secondhand smoke exposure gave them cataracts as adults. Basically, if you perform enough studies looking at enough variables for enough conditions enough times… you can almost always count on finding at least SOMEthing out there to blame your favorite bete noir on.
– MJM

Down here, getting “blind” has a different reason, and that reason is far more connected to CO2 than the climate reason !! hic !!!

Lark says:
One sees a lot of this in medical studies. “Power lines cause cancer!”
Thank goodness they are not very edible or smokable, then !!

“Cut off the money supply and you cut off the BS.”
Hey, not yet….. once I finish my current task, I was sort of hoping to get myself some of those funds.
I could have issues if they ask what my position is on climate, but I’m sure I can manipulate my way around theat, been watching and learning from the ‘climate scientists’ 😉
Just have to learn to lie and distort the thruth, is all, then I’ll fit right in. 🙂

“That means everyone in Queensland is legally blind…..”
and in Darwin.. just “blind”
http://www.dailytelegraph.com.au/archive/national-old/northern-territory-drinks-most-alcohol-in-the-world/story-e6freuzr-1225735132584

Congenital cataracts? It’s worse then we thought!
Sorry, couldn’t resist.

The use of multiple significance tests is a well known problem in statistics. The use of many variables is problematic if they are correlated. Else use the binomial distribution. Its expected value Np is already telling. With p = 0.05 and N = 28 we expect 1.4 false positives among independent trials. I once tried to explain that to a researcher who used 60 variables in 60 significance tests and found 3 ‘significant’ results. I did not succeed and the same BS is still around. Problem seems to be very difficult.

Pnce again poor science gets ‘Eschenbached.’

Someone I know very well and has an important role interpreting published science in a medical area for public policy makers, tells me that 90% of the published papers in his field are worthless because of problems like this. He tells me, most of it comes from doctors, who think they are scientists.
I googled 2 of the authors and both are epidemiologists. A discipline one would expect to know something about statistics.

I won’t bother to read the daft paper then! More seriously, this is exactly the kind of crap science we have come to expect. What bothers me is that this passes muster and is published.
Also, in relation to say, something that is non-linear and semi-chaotic in nature, I dunno, let’s say, something like ‘climate’ – it would be relatively easy to pick a variable and any number of the vast number of climatic ‘events’ and ‘interdependent variables’ and probably make a case for one ‘important’ variable being the sole cause of said events……Oh wait, now I get it, that’s why CO2 is repsonsible for everything!

First off they used regressive analysis which can show a false correlation with one variable and they had several. Second, they cannot prove causation as this was not a controlled study. Maybe I don’t have the background to understand these statistical methods? The Rahmstorf and Foster paper also used a method similar to this one to get their “temps match IPCC” graph and I recently read a study on second hand smoke that was touted in the media as proof of the dangers and it was actually a meta-analysis which really blew my mind. I just finished my first stats course and from just playing around with some regressive analysis and sampling I was able to see the inconsistencies with these methods. If you don’t like your results, take another sample, remove outliers and try again, change the scale of your scatter plot. Lots of room to introduce bias…

I remember getting taught about the Bonferroni correction (and other options) for this multiple testing problems in my first-year undergrad general stats course about 20 years ago. It’s a very well known problem. Surprised (well, not really) the researches and none of the reviewers picked it up.
I hate to be picky, but 2 points on statistical terminology. In statistical significance testing, the p-value isn’t the probability it happened by random chance, but the probability of observing such a extreme result if the null hypothesis is true. If the null hypothesis is ‘random chance’ then it’s the same thing, but if I’m comparing two groups, 1 null hypothesis might be there’s no difference between them, but an alternative null hypothesis is that there is a difference of a specified amount. For the same data, I’d get a different p-value depending on which null hypothesis I’d identified a-priori as the one to test.
Secondly, a p-value is a probability, not an odds. An odds is the ratio of the probaility of success to the probability of failure, ie p/(1-p). While people often use them interchangeably, in statistics they’re 2 different things. Admittedly there’s not much difference if you’re talking about a probability of 0.05, but if the probability is 0.75, the odds is 3 (ie 0.75/0.25).
Here endeth the lesson. 😉

As usual, Mr. Eschenbach got things wrong.
There’s actually no problem with his article, all the math he presents is perfectly correct.
What’s wrong are his assumptions on methods used in the PubMed paper which he apparently didn’t read carefully enough.
First of all, while the paper is inspired by IPCC conclusions, it is not about ambient temperatures at all. It is about heat waves and it even gives detailed description about what is considered a heat wave. And that definition makes pretty good sense both in New York and in Phoenix.
Second, while Mr. Eschenbach is calculating how likely it is that such result may come out of totally random data, he might have also calculated it for the results they actually present. That means, what is the total statistical significance of one result out of 28 having p<0.05 at three separate criteria at once, one result having p<0.05 at two at once, and remaining 26 being not statistically significant in any of the three criteria. Because done correctly, that state coming out of random data is actually pretty unlikely.
That article does not deserve such kind of treatment from Mr. Eschenbach, it is an interesting study. It may not be relevant for global warming for all of the reasons which were discussed on these pages thousands of times already but it still may be relevant to what people may want to care about during pregnancy.

I concur with Kasuha’s comment above. This is a hypothesis-generating study and the authors stress the need to confirm the possible association in further studies. The authors seem to be well aware of the issue raised by Willis Eschenbach since they discussed it themselves: “Last, because we performed multiple tests to examine the relationships between 28 birth defects groups and various heat exposure indicators in this hypothesis-generating study, statistically significant findings may have been attributable to chance. Under the null hypothesis, we would expect 4 of the 84 effect estimates displayed in Table 3 to be statistically significant at the p = 0.05 level. […] However, the associations with congenital cataracts are biologically plausible, particularly given stronger associations during the relevant developmental window of lens development, and associations were consistent across exposure metrics, making chance a less likely explanation for these findings.”

These people are no more scientists than some people in the banking sector are wealth creators; where wealth = something of value and not just a symbol.

Whoever reviewed this paper made a terrible brainless job. It is usual standard in biomedical epidemiological research to apply Bonferroni correction for multiple comparisons. Thus , agreed, the methodology is a nonsense. I personally review papers for top ranked medical journal (e.g. NEJM and JAMA) and I can assure everybody that a paper like this has no chance to be published in those journals.
Most importantly, statistics means nothing if there is no reasonable pathophysiological hypothesis to explain the finding (which actually should be the reason to perform a study).
BUT there is even more fun in this paper. According to table 3 there appear to be a protective effect of temperatures for the onset of anophthalmia/microphthalmia (OR 0.70 CI 0.52-0.93). So, if you heat a pregnant woman you have more chance of cataract but less eye gross developmental problems. Since cataract is curable and anophthalmia is not……. voting is open.

Hi Willy,
Your argument is dead on the money. It’s simplest to just look at p itself — given 28 throws of a 20 sided die, one expects 1 (FP) to come up. Slightly more than one FP to come up, in fact. But this is not all.
In order to properly assess the impact of FPs, one has to use Bayes’ Theorem. The problem is that a broad population analysis of this sort requires one to know a lot about the prevalence of the diseases in question — both rate and details of distribution. So what, exactly, is the prevalence of congenital cataracts? Google to the rescue. In at least one study I just grabbed from the UK, prevalence was reported to be somewhere between 2.5 and 3.5 births per 10,000, depending on how long one waited to make the diagnosis. This distribution is not flat — as one article I found puts it: Congenital ocular anomalies are major contributors to childhood visual morbidity. Congenital cataract is one of the few of these visually handicapping disorders that is amenable to primary prevention—for example, through a rubella immunization program…
In other words, there are problems beyond applying a 0.05 “significance” test to a shotgun blast of studies. The disease has a fairly low prevalence and it has at least one significant confounding factor (whether or not rubella is prevalent in the community). Are there other confounding factors? From another study (all of them available unpaywalled online, thankfully): In southern India, the prevalence of congenital cataract is estimated to be 1 to 4 cases per 10,000 children examined.4 A major portion of these is hereditary, or genetic, in origin.
We see that the disorder is not only associated with whether or not the parents and community have been adequately immunized against diseases — something that for better or worse is unevenly distributed worldwide with temperate zone nations having a much higher degree of immunization coverage than tropical ones — but that the prevalence in southern India is almost exactly that observed in the UK. Now, I lived in northern India (New Delhi) for seven years in the 60s, right across that “Ice Age Cometh” dip in temperatures that had people worried going into the 70s, and it went down to freezing precisely three times in seven years. In southern India it didn’t get close — it is hot (even compared to northern India). New Delhi is easily 5F warmer than any point in the UK, on average — Bangalore is probably 10F warmer than the UK.
We also have the fact that, of the cases reported, at the very least many of them are hereditary — not caused by the heat but caused by inherited genetic factors that run in families. In fact the second article was looking at: The authors focus attention on congenital lamellar cataract, which is associated with the R168W mutation in γC-crystallin, and congenital zonular pulverulent cataract, which is associated with a 5-bp insertion in the γC-crystallin gene. — specific mutations. This is real science, of course, not shotgun-blast population studies.
I could continue to search, but I think it is already pretty clear that two things are true. First, prevalence is not particularly accurately known. In the UK, with first world medicine, they fail to diagnose 1/3 of the cases observed before the individual is 15 years old (hence the rise in prevalence with the age at first diagnosis). The prevalence there is slightly less than 4 in 10,000. In India the prevalence — which is likely to be reported less efficiently early on, but one presumes that even in India blindness is blindness and by age 15 a diagnosis is likely — is given as 1 to 4 in 10,000, which sounds like the final prevalence is almost exactly the same as it is in the UK!
Now let’s think about this: 6,422 cases and 59,328 controls. What exactly does this mean? In 60,000 iid samples, one would expect to get 24 cases of congenital cataracts. Sigma for this is ( for ) 5. Two sigma is 10. Sigma is commensurate with the expectation value — the worst possible case for drawing reliable conclusions.
To put it another way, suppose that only 15 people got congenital cataracts in this control group. Would that justify the conclusion that the prevalence in the general population is really around 2 in 10,000, not 4, just because it is only a 5% likely occurrence? Don’t make me laugh. Similarly, a prevalence as high as three sigma would be no particular cause for alarm, especially if the group were truly randomly selected so that the samples were reliably iid (it never is, but that’s another story).
Now consider the “case” group. 6000*0.0004 means that 2.4 individuals are expected to get cataracts in this group! Sigma is now almost 2. There is almost no rate at which this group could get cataracts that would be worthy of attention. Statistics just doesn’t work for small populations with a low prevalence.
This may not be the structure of the study — they could be trying something even sketchier than this — but one can, as you note, take great comfort in the large scale numbers between people who live all the time in hot South India versus the people who live all of the time in the cold UK. The prevalence in the two populations is more or less the same, with millions of individuals contributing to the prevalence numbers in both cases. End of story.
One is reminded of a famous study involving cancer rates of people living near high voltage transmission lines that was conducted in just this fashion, and got precisely this kind of result — examine a small population (compared to incidence rates of cancer) for all kinds of cancer. Some forms of cancer will (in the small population) always happen less frequently than expected, some about the expected rate, some more than expected. Look at 20 to fifty cancers, and at least one or two are going to be at rates unlikely at the 0.05 level. Publish, generate lots of alarm, and it takes years to get fear of living near a power line out of the collective minds of the people. Or what they are doing now with cell phones, ditto. Big, expensive, careful studies refute this kind of bullshit statistics but it takes years.
We should talk about p in random number generator testing sometime. dieharder, my test suite, generates a list of some 80 or 90 test pvalues. One cannot use anything as puerile as 0.05 rejection, because one expects four or five tests to fail at this level every run with a perfect random number generator! In fact, if this many tests did not fail, on average, one would be certain that the generator was not random!
Statistics is a two-edged sword!
rgb

This study indeed seems like a classic example of the “Data dredge”, well described by John Brignell: http://www.numberwatch.co.uk/data_dredge.htm

“one-in-a-million chances crop up nine times out of ten” (Terry Pratchett, “Equal Rites”)
Also http://tvtropes.org/pmwiki/pmwiki.php/Main/MillionToOneChance (warning: seriously addictive site)

Why should we worry when global warming should be felt mostly in winter? Uhi on the other hand is something else.

Sounds like vitamin D deficiency to me… Fixing it is no different than fixing Spina bifida with folic acid at the right dosage.,
http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(96)91331-8/fulltext
Based on this study, pregnant mothers do much better at the daily dosage of 4,000 IU vs what is recommended 600IU which isn’t even enough to push us into optimal range of vitamin D level. No wonder why we get sick with cold or flu or infection easily during the cold months…
http://grassrootshealth.net/index.php/press/92-press-20100430
One could easily say that this disease happens more often in NY than AZ is because of long winter forcing us to stay in the house and not see sun much compared to AZ where winter is short and sun (specifically UVB sunlight) is more intense to help skin produce vitamin D… All you have to do is conduct vitamin D study. Very simple study.
Anyone even aware that during the midday with clean air in the summer in bathing suit without sunblock gives you 20,000 IU of vitamin D after 20-30 minutes (for Caucasian people)? The darker your skin gets, the longer it takes to reach 20,000 IU. It can be 10x longer for people with darkest skin color. The recommendation of 600 IU a day makes no sense. Someone made that recommendation on faulty science a long time ago. It used to be 200 IU a day 20,30 years ago. We paid the price…

If they really want to motivate action, they should have released a study, whose results would indicate AGW causes a reduction in penis and breast sizes. With society’s fixation on this parameter, there would be an instant demand for a new LIA.
We really are a strange and predictable people. GK

Jason T, it is good to see that they did a better analysis than appears “at first blush” as you put it, but they ignored the elephant while focusing on the mouse. There is nothing in science that is more dangerous than fishing for hypotheses in some vast pile of highly multivariate data. It is, in fact, a rather complicated logical fallacy.
The elephant involves — as Willis (and I) point out — simply looking at accurately known prevalence rates drawn from large populations that live all the time at temperatures that vary by 5F or more. Of course one doesn’t expect those prevalence rates to match perfectly even for large populations drawn from different parts of the world, because the population genetics is generally different, the background of confounding variables like immunization, exposure to teratogenic substances, radiation levels (which are a function of height above sea level, latitude, and specific environmental factors local to particular places) are all different. So even if they were different for these huge populations one’s utter inability to control for the confounding variables (most of which are simply unknown and unknowable) makes it impossible to resolve a temperature-based difference, although one can place an upper bound on it quite easily. A very, very small one.
To do better, one would require all the data in the world. Literally. Then one could coarse grain it (splitting the globe up with some appropriate tesselation of the sphere), visit all the tessera, measure or estimate all of the confounding factors and the mean temperature, and do god’s own fully multivariate statistical analysis. Ultimately, you’d need to show that temperature has unique explanatory power not confounded by mere accidental covariance with e.g. ultraviolet exposure in tropcial climates, pollution in industrializing third world countries like India (which is currently phenomenal) and so on.
As for the author’s argument that this is somehow “plausible”– humans are homeothermic animals. This means that it does not matter as a general rule what the temperature outside your body is — inside, it is 98.6F plus or minus half a degree F. When it is higher, we say that the individual has a fever. When it is lower, we say that the individual is hypothermic. Both are serious conditions, no doubt, but fevers occur routinely and no doubt occurred throughout the two populations throughout pregnancy at a more or less normal rate, which makes it rather probable that individuals in the populations had a fever for at least a few days while pregnant.
And here again, we have a very, very simple statistical test, one that the authors would surely have investigated if they actually gave a damn about being accurate rather than creating hysteria. For example:
http://www.healthline.com/health-blogs/fruit-womb/fever-pregnancy
What? Fever (hyperthermia) during pregnancy is associated with birth defects? Damn skippy it is! And what is the most common cause?
Over the years, several studies have confirmed that temperature elevations in pregnant women accompanying influenza and common cold virus infections are associated with greater risk for congenital anomalies, multiple and isolated, especially, neural tube defects.
Wait, wait, did he say neural tube defects? Not congenital cataracts? So that if the women in question actually had elevated body temperatures (hyperthermia) from having been in a warmer climate for one whole week of their pregnancy (snowbird trips to Disney World during pregnancy now being counterindicated) then it is almost certain that an increase in neural tube defects would have been detected before an increase in congenital cataracts?
Oh, and while we are at it, why not look in the literature at the connection between actual fever-induced hyperthermia and congenital cataracts? Let’s see:
http://eyewiki.aao.org/Congenital_and_Acquired_Cataracts_in_Children
The causes of infantile cataracts have been the source of much speculation and research. Making a distinction between unilateral and bilateral cataracts may be useful when considering etiology.
The majority of bilateral congenital or infantile cataracts not associated with a syndrome have no identifiable cause. Genetic mutation is likely the most common cause.
and
Systemic associations include metabolic disorders such as galactosemia, Wilson disease, hypocalcemia and diabetes. Cataracts may be a part of a number of syndromes, the most common being trisomy 21. Intrauterine infections including rubella, herpes simplex, toxoplasmosis, varicella and syphilis are another cause.
Then there is this one:
http://www.ncbi.nlm.nih.gov/pubmed/16323161
(from 2005). Conclusion: Some isolated congenital cataracts are preventable by rubella vaccination and probably by influenza vaccination in the epidemic period. In addition, our results suggest that using antifever therapy for fever-related respiratory diseases may restrict the teratogenic risk…
although also (and very interestingly):
A higher prevalence of influenza or common cold during pregnancy was found in the case group (55.9%) than in the population control group (18.5%; adjusted odds ratios [ORs], 5.8; 95% confidence interval (CI), 4.0-8.4) or in the malformed control group (21.7%; adjusted OR, 4.7; 95% CI, 3.2-6.9).
In other words, a fever during pregnancy almost certainly is a causal factor in congenital cataracts! And what are the most common causes for fever? The flu or the common cold, although rubella (in places where MMR vaccination is not routine) or any other illness likely to cause a high fever increases your risk by a factor of three (more if you adjust it to reflect the gaussian distributions).
OK, so this is probably enough. We can see that sustained hyperthermia during pregnancy can very probably cause congenital cataracts as a birth defect outside of its association with familial inheritance or e.g. trisomy of chromosome 21. So, is sustained hyperthermia associated with “heat waves” in the summertime? Do people get a “fever” when it is hot out?
And what about flu and the common cold? Are they more likely when it is hot outside?
People do indeed become hyperthermic during a heat wave — primarily the very old and very young. Hyperthermia is defined in this case as a body temperature over 100F (sustained) without having a fever, that is, without an external cause such as a virus. It isn’t normal — as I said, people are homeothermic and unless they are stressed or exposed in an unrelenting way to higher temperatures (such as working out in the sun, engaging in outdoor sports in the sun) without access to water or shade on a high-humidity day they tend to stay under 100F even when it is hot out. Extended exposure leads to “heat exhaustion” or “heat stroke”, with symptoms very similar to those of correspondingly high fevers.
However, pregnancy is not listed as a risk factor in getting heat exhaustion when it is hot out. It isn’t an implausible one — women are under stress, their surface to volume ratio changes, their baseline metabolic rate can be higher — but surely heat exhaustion would show up in the individual case histories of the people in the study, not just “exposure” to high temperatures. Where I live (North Carolina) it is always five degrees farenheit warmer than it is in upstate New York (where I also lived for a long time). But here we have air conditioning, or fans, or the sense not to sit outside in the sun on a hot and humid day when we are pregnant (if we can avoid it, and most can not to protect their baby but because being hot and sticky is uncomfortable).
Then there is the flu/cold factor. According to the CDC (see e.g. here:
http://www.cdc.gov/flu/weekly/index.htm#MS
one is hundreds of times more likely to get the flu in the winter months than the summer. The cold (also associated with the birth defect, recall) follows a very similar pattern — summer colds are rare, winter colds are commonplace. In fact, almost all the respiratory infectious diseases seem to peak in the wintertime. One’s chance of having a fever in association with these diseases is therefore many times higher in the winter than in the summer, which one would expect to partially or completely cancel any general hyperthermia-linked bump in the summertime.
In other words, even if the perfectly reasonable hypothesis that having an elevated core temperature for an extended time while pregnant is teratogenic and could result in a variety of birth defects, with neural tube defects leading the way, the risks of febrile infections peaks in the colder months and must at least partially counterbalance the risk of hyperthermia due to excessive summer heat. Indeed hyperthermia is a lot less likely to be sustained for periods longer than a few hours, while fevers can last for days, so one would expect it to be a smaller factor.
Of course, in both cases simple common sense measures can provide protection. Get a flu shot before you get pregnant, and avoid sick people while you are pregnant. If you get sick anyway, use an approved anti-febrile agent to reduce your fever until you get better. If you are pregnant, it isn’t a good time to play tennis on a sweltering day, and you need to drink plenty of fluids.
That takes us down to the final bit — linking this to climate change. First of all, there isn’t any evidence that the climate is “changing” at this particular moment. The weather every year is somewhat different, yes, there are heat waves and cold spells. If you look at the cdc data above, you’ll see that there were eight times as many pediatric deaths from flu in the cold winter of 2009-2010 than there were in last year’s mild winter, just as there were more deaths of heat stroke during last summer than there were in many a comparatively more mild summer in years before. Weather extremes of any sort cause distinct problems, and weather extremes happen somewhere all the time and everywhere some of the time. There is no evidence that these extremes are changing their distribution. There is no evidence that the climate is “currently” warming, where the meaning of the word currently with respect to climate is a rather sticky question (averaged over just what window).
But the bottom line is that the paper above produces a weak statistical link — one that inside the paper it is acknowledged to be too weak to form any sort of conclusion, because they saw the incidence of a different sort of birth defect go down in the same population at the 0.05 significance level, and it is as absurd to assert that the warmer weather was protective of one as it is causative of the other. But the paper title says otherwise, and it has already been grabbed and dumped onto environmental sites as “proof” of one more danger of CAGW. No doubt this will get added to the absurd epidemiological studies that claim to tell us how many additional people die every year “because of AGW” (count the begged questions) while they never seem to subtract the people that lived “because of AGW”, like the 250 extra children who didn’t die of the flu in 2011-2012 compared to 2009-2010, or the people who didn’t starve to death because of a premature frost in farm country.
Given the flaws, the paper should not have been accepted, not with its title. Basically what they showed is that there are no statistically significant correlations between “heat waves” and birth defects visible in a study of this size. This is strongly supported by a two-minute analysis of prevalence rates in different climates plus the fact that neural tube defects (the “coal mine canary” as it were) should have the greatest sensitivity to hyperthermia from all causes and was not observed to bump, something they failed to look at and that forms a powerful Bayesian prior that still further reduces the probable significance of their result. Their title claims otherwise.
Sadly, their title is understandable. Nobody wants to publish null results (even though, as Feynman pointed out, often they are the most valuable results to publish). Who will fund further work if you don’t get anything the first time around? And here, they get to tie their work to not one, but two demons — birth defects and the horrors of Global Climate Change. Funding for more detailed work is assured, and if that future work produces a null result, not a single soul in the lay population will ever hear a word about it — the urban legend is already established and this paper will never die.
rgb

Doesn’t the temperature of a mother’s womb stay roughly the same even if there is a 5-degree [F] increase in summer????
Causes of congenital cataracts.
http://www.nlm.nih.gov/medlineplus/ency/article/001615.htm

E.M.Smith says:
December 19, 2012 at 8:53 pm
Just Amazing…
Another case of “Climate Science” done by folks who took one Stats class, then forgot most of it….
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I doubt they even took a statistics course at all depending on the school. Probably some prof. spent a day going over how to use a stat. computer program and that was it. (Based on my undergrad required courses in chemistry and the one day intro to the field of statistics in an analytical chemistry class.)
For example a Biological Sciences major at Illinois State University (link) requires NO STATISTICS at the undergraduate level but does require a course in Ecology and Biological Diversity.
In the masters program you finally get one course **BSC 490/420.27 Biostatistics/Biostatistics Lab – 4 Credit Hours and an elective of BSC 450.37 Advanced Studies in Biostatistics – 3 Credit Hours
BSC 490/420.27 Biostatistics/Biostatistics Lab
This is a graduate course introducing students to applied statistics and data analysis using SAS. The goal is to prepare graduate students for using and understanding common statistical methods in Biological Sciences.
Actual course outline link

COURSE GOALS: This course is an introduction to applied statistics. The ideas and methods discussed will be those most relevant to biologists in general. You will acquire a working knowledge of basic statistical methods, and will be able to determine which procedures are most appropriate for a given circumstance. All of the statistical techniques relevant to biologists cannot be covered in one semester, however, once you have mastered the material in this course, you will be better equipped to understand and use more advanced statistical methods.
In the laboratory portion of this course you will gain experience in the use of the SAS computer package for statistics. There are a number of good statistical packages available, and some of you may already know how to use some of these. I will give examples and explain how to do things in SAS, and all of you will do the assignments using SAS. By learning enough about general aspects of statistical computation and interpretation, you will be able to generalize to other packages if you so choose.

Depending on the teacher this could be just a course on how to plug numbers into a computer with little fundamental information on the correct use of statistics. The reading assignments do look reasonable however.

John West says:
December 19, 2012 at 11:40 pm
Forgive me Willis for not sharing in your amusement…..
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Occasionally we have to laugh to relieve the stress of realizing how very serious this whole subject is. It keeps sceptics from going postal. (High moral standards also do that)

D Böehm says:
December 19, 2012 at 10:15 pm
Being transparent to RF frequencies means that RF energy is not felt by our bodies’ cells. The cell phone/cancer scare is as fake as the AGW scare.
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Water is not transparent to RF. It is the principle behind microwave cooking.
HAM (amateur radio) warns of the dangers of transmitting with an antenna held near to your head. This warning is given repeatedly in bold faced type, in all certification manuals and at all levels.
The problem with new technology is that we have not been genetically selected for adaption to it. Thus, by chance some individuals will be killed off by the technology until the gene pool consists mostly of those individuals that are resistant to any potentially harmful effects. A similar experiment with artificial fats introduced during WWII created an epidemic of heart disease in some western countries. Those countries that avoided the artificial fats, such as France, have low incidence of heart disease while eating a high cholesterol diet.
Yet to this day, while many scientists recognize the connection between fat and heart disease, they fail to recognize that only certain types of fats are a problem, and only to those individuals that are genetically susceptible. Those fats that have been eaten for many generations will not be a problem, because our ancestors would have long been killed off by them and we would not be here.
These fats may even have a protective effect, by blocking receptor sites in the body that would otherwise be occupied by artificial fats. Thus, by stressing a diet low in fat, scientists may in fact be giving the wrong advice and the wrong treatment. The Mediterranean diet shows that the cure to heart disease may well be to eat more fat rather than less, so long as the fat is the same as what your ancestors ate.
There are many parallels between this situation and climate science. Faulty scientific conclusions based on faulty use of statistics, with a cure that may be more harmful than the disease.

Lewis P Buckingham says:
December 20, 2012 at 3:23 am
…Further, quoting animal studies, it should be remembered that congenital cataract formation is a feature of some lines of dogs, especially in the cocker spaniel and has a strong genetic component….
>>>>>>>>>>>>>>>>>>>>>>>>
Just to add, it is very strong in some lines of horses/ponies esp. the Appaloosa. Appaloosas are one of a few breeds that can have cataracts present at birth. and link 2

This is another example of poor meta-analysis from the department of spurious statistics. I spent 30 years in the pharma industry closely involved with clinical studies on drugs. An area were meta-analyses abound! As examples we have all heard of the supposed correlation (or not) between electromagnetic fields and cancer and HRT and cancer. Both the result of many confused meta-analyses with the golden grail of a P<= 0.05 generally not worth the trees they are written on. Good analyses require prospective studies which address the issues of confounding variables and which designed so that the outcome of the study is blinded to interested parties until the trial code is broken. Even in this case P<= 0.05 would be considered borderline and strong correlations would need at least a P<= 0.01. In open and uncontrolled meta analyses studies these spurious P factors are worthless unless they are used to indicate the direction of further real research. This principle also applies to most, if not all, meta-analyses used in climate science which as far as I can see rarely show high statistical correlations and since there is usually no attempt to control or analyze confounding variable are again not worth the multitude of trees, both as subjects of analysis and paper used to write them on. Unfortunately this also applies to both pro and anti global warming camps ( just in case this upsets anybody I'm strongly anti!). This is also the issue which divides Leif from the rest of the anti groups who regularly feature on this website. As I understand it Leif's view is that something highly significant is happening to the sun right now based on his observations however he believes he has yet to see any clear causal parameter which links changes in the sun with the climate on the earth. That's a rigorous position to take but I think it lacks any proposal for the linking mechanism i.e. a testable hypothesis. In the anti-Leif camp correlations abound without any testable hypothesis which might indicate causality with the exception perhaps of the Cloud Experiments which I assume are still on going.
Finally, in the areas of science were meta- analysis abound you find frequent, invalid, attempts to slice and dice the data to get a better fit (retrospectively) like infamous the tree ring studies ( not worth a tinker's cuss) and switching the basis of the analysis.- the hiding the decline method all of which have nothing at all to do with the science and all to do with protecting reputations, maintaining grant funding or political agendas (both non government – the green lobby and government – distraction from the real and difficult issues affecting the state) such is the poor state of climate research. Ho Hum!

There is actually a wonderful opportunity here; of the 28 groups, one showed a predicted increase with increasing temperatures. But it’s likely one or more also showed a predicted decrease (!) of birth defects with increased temperature. A request for the raw data from the researchers will confirm this. If in fact it is the case, one would wonder why the BENEFITS of warming were not reported!
There was and they were. Sort of. The paper correctly — at some point — notes that the data don’t suffice to show either one. They call — naturally — for more work to be done because of this. But AFAIK they didn’t do a full KS test on the distribution of p from all 28 tests (which wouldn’t have been very reliable, but might have been revealing in its own right even as a population histogram). The most important question is whether or not this distribution is or is not more or less flat. If it is flat enough that the KS test yields a reasonable p-value, one cannot reject the null hypothesis of “heat waves do not cause birth defects” just because one birth defect in the distribution tested came out with any particular value of p.
Here is a really lovely thing I remind people who don’t understand hypothesis testing. It is just as likely to get a p-value in between 0.475 and 0.525 — which nobody would reject — as it is to get a p-value in between 0.00 and 0.05, for any test where the null hypothesis is statistical independence so that outcomes are randomly distributed. You would be precisely as well-justified in rejecting a random number generator, or any other process designed to produce a p-value (that is, if correctly done, a uniform deviate per test) because it gave you a number in the former range as in the latter.
Hence the strong need for KS testing of the distribution of p, or the insistence that a p value be really low to reject the null hypothesis, not 0.05. I don’t even like 0.01. 0.001 is OK, depending on how many samples you plan to draw, but I play with random number generators far more than is healthy for any fully grown man, and hey produce numbers smaller than 0.001 — one thousandth of the time. I like to reject null hypotheses when p from a good KS test applied to the p-values produced by many independent “runs” of some testing process gets down to or so. That’s starting to be pretty unlikely, and the distribution of p in that case would almost certainly be visibly non-uniform.
Or one can (sometimes) do one’s stats on the cumulated data and get to the same place, even more accurately. In this case one can easily do this by comparing population prevalence between tropical and temperature climate countries. If the paper we are discussing is correct, it predicts that there should be at least a 50% greater prevalence in the former than the latter, absolutely clearly resolved. Furthermore, one should be able to associate the outcome with specific cases of clinical hyperthermia, hyperthermia that somebody actually measures, and relate the surplus to increased risk due to e.g. fevers in a quantitative way.
It isn’t that the claim is falsified by the study. It is that it is absurd to claim that it is verified, and a meta-glance at the overall data suggests that it is unlikely and shouldn’t even be thrown out as anything but a null result without answering a lot of questions that this study does not answer.
rgb

HAM (amateur radio) warns of the dangers of transmitting with an antenna held near to your head. This warning is given repeatedly in bold faced type, in all certification manuals and at all levels.
I looked at this, in detail. The issue is one of skin depth at the frequency in question, power in the transmitter, the utter lack of resonant transitions (this is not ionizing radiation). A cell phone is about as dangerous as a flashlight held against your head. Less dangerous than a powerful flashlight. Or going out into the sun. The sun is way, way more dangerous — it actually does hit you with dangerous ionizing UV (which is indeed carcinogenic in measured ways) in addition to some 300 to 700 times as much broadband electromagnetic intensity as your cell phone. The skin depth at the frequency in question is around 1 cm, meaning that most of the less than 1 W total power doesn’t even make it inside your skull, and becomes a warming “noise” utterly indistinguishable from your baseline core thermoregulated temperature.
I’m a physicist — if you want me to believe in a cell-phone — cancer connection you’re going to have to show me how the radiation can degrade DNA. It ain’t doing it by heating it. Again, beware results at the edges of “statistically significant” by the paltry 0.05 standard, pulled out of shotgun population studies or advanced by people infected with confirmation bias and with a dog in the race. Beware even then of confounding factors — maybe cell phones leach heavy metals into the air while operating and it is these that are toxic, or people who own cell phones are likely to drink coffee decaffienated by rinsing with an organic solvent that remains residual in the coffee and causes cancer. Correlation isn’t causality, and causality has to be physically plausible if not verified. Show me cell phones degrading DNA at standard powers from 2\pi steradian solid angle radiation at a distance of 2 cm and through a layer of watery skin and bone.
rgb
REPLY: I agree. As a broadcaster, we don’t have such warnings mandated for people who work around TV/radio transmitters where ERP is in the tens to hundreds of kilowatts range. If there were issues at VHF/UHF frequencies and power we routinely deal with, surely the FCC would have mandated warning labels. Microwave frequencies typically will cook your eyes before your brain (a standard warning from my days working on S band and C band weather radars) with pulsed ERP’s in the megawatt range, so I tend to laugh at the worries over cell phone and WiFi router radiation int he milliwatts range. I’m also an amateur radio operator, and with the exception of a few worry worts in my circle, few care about the issue. Note that police and firemen carry the same sort of equipment daily, and if there was some provable cancer causality, you can bet the police and fire unions would be all over it for improved health care/risk benefits, but they aren’t – Anthony

One final reply that says it all — in fact, pulled from the link on the wikipedia page on data dredging — that could have been 100% of Willis’ article above as one series of pictures is worth a few thousand words:
Obligatory XKCD — jellybeans cause acne. Pardon me, only green jelly beans cause acne.
http://imgs.xkcd.com/comics/significant.png

Some excellent discussion above of p values and their meaning. 🙂 Heh, if I took a pair of dice and rolled snake eyes on my first roll, should I label the dice as biased? They’ve obviously produced a result with a rather small probability of occurring by chance after all!
One thing to guard against that I believe you have not yet been hit with: moving the acceptable p value for public policy decisions from .05 to .1 — doubling the chances of the result being due to chance. Don’t get too comfy though: the EPA did it for “passive smoking” in their 1992 Report that ushered in US smoking bans and they can just as easily do it for studies regarding climate change. All they have to do to legitimize it is ignore studies going in the contrary direction and declare that it is “agreed” that causal results (of something like CO2 and warming) can only go in one direction. That will then allow them to use a “one-tailed” analysis, doubling the p-value. The media tends to be uneducated in such things and will just take the word of “the authorities” as being correct: a 90% finding will then be hailed as “statistically significant proof” and the hole will be very difficult to dig your way out of.
rgbatduke: Very interesting info on solar radiation! I’ve used the “less dangerous than sunshine” analogy many times in my writings on smoking bans by comparing the need to “protect the workers” in outdoor dining patio situations: after all, as the true believers like to say, “Why should THEY be the only workers forced to risk their lives for a paycheck? (Outside venues) are neither inherent nor necessary for dining or drinking. Serviced patio dining needs to be banned!” But when I’ve made the argument it was based mainly on risks of skin cancer. I had no idea that there might be an even more bogus cell-phone-connected argument to make! LOL! Hey, does cell phone use contribute to global warming???
:>
MJM, Cell phone and sunshine exterminator! Reasonable rates. Termination with extreme prejudice incurs extra charges. Ask about our “80% statistical significance special” (available only on alternate Tuesdays.)

rgbatduke says: “Jason T, it is good to see that they did a better analysis than appears “at first blush” as you put it, but they ignored the elephant while focusing on the mouse…”
Just want to say I always enjoy your comments.

Willis Eschenbach says:
December 20, 2012 at 2:31 am
…
So while you are correct that the odds of that particular state are small, the odds that we’ll have at least one result of p = 0.05 are quite large, as I calculated above
w.

Yes, just to make the point clearer, you’d have to pick the specified significant variable in advance of doing the study to get kashua’s result. Saying, “This variable will be significant” is a much tougher standard than “Some variable will (appear to) be significant.”

I’ve learned so much from your guest posts here!
I’m wondering about blindness in New York city. The other day, I was watching a cable channel that was giving the projected high temps for the New York City area. I don’t remember what they were, but the projected “HI” for the city was 10 degrees F higher than the suburbs – I guess this means that kids in the city are all going blind and God only knows what all else.
Seriously, if they’re going to do studies like this, it seems a lot smarter to test people living in and out of the urban island effect in the same region.

Statistics is bloody difficult. He says sitting here with three books on R and Bayesian analysis and two books on javascript at hand, with a dozen more in the house and work, trying to learn R and javascript both so I can learn to use mongo (huge database program with a huge database that speaks JSOL) and R in order to do predictive models both within R and with a proprietary neural network I wrote, once I integrate the db contents with all three. Yesterday. I know a lot, and I understand its basis (which is worth more), but there are huge chunks of stats I still don’t know, or learn only as I need them. I’m at the bleeding edge as far as Markov processes are concerned and multivariate predictive modeling is concerned, way less expert in boring old regression, pretty far out there in Bayesian analysis, and yet there are plenty of tricks used in mundane stats I’ve never used and don’t know. And I can do calculus and algebra and all that (as well as code, obviously, in too-many-languages-to-count+2 new ones, truly expert in C).
Pity the poor suckers like Michael Mann who actually wrote a PCA package in fortran so he could do his tree-ring work. Along comes M&M who actually use, um, R (because other people wrote it, and those people are really, really good at statistics and keep rewriting it and improving it and adding onto it as bugs are discovered, and besides, it’s bone simple to use once you’ve learned its data structures as it does nearly anything you want, the way you should be wanting to do it, automatically and by default once you get the data loaded in at all) and show that his results are bullshit with a few well chosen simulations and exercises.
Back when I was a grad student and early postdoc, I used to write my own numerical code (and yeah, usually in Fortran, as that was sort of the default physics language in those days). Fortran on IBM mainframes, fortran on PDP 1s and 11s, fortran on a Harris 800 (with the Vulcan operating system no less and 3 byte word boundaries), fortran back on a mainframe, fortran on an original 64K motherboard IBM PC, fortran on several AT clones before I converted to C in 1986 and never looked back. My excuse back then is simple — numerical libraries cost a fortune and nobody had them. Nowadays, I use the Gnu Scientific Library and try NOT to write my own code for standard stuff like statistics (although I’ve written a ton of stats routines in the past) because all it takes is one boneheaded bit of code and you’re screwed — garbage in OR garbage code equals garbage out.
Pity also physicians who are trying to do research that involves a serious statistical analysis. One stats course does not an expert make, and that is precisely what most of them have. If that — a lot of them took calculus in University, not EVEN one course in stats. As a consequence, they probably have never even heard of “data dredging”.
If they had, they would never claim that green jellybeans cause acne in a snipe hunt involving 28 possible hypotheses against one binary variable. In fact, they wouldn’t publish the results of the snipe hunt at all — they might legitimately use it to formulate a hypothesis to be tested against completely new data after looking carefully at some of the issues listed above, e.g. the lack of meaningful or corresponding differentiation of total prevalence (already well known) with climate, which suggests that if the effect they observe is not cosmic debris, a purely random occurrence as it is more than likely that it is straight up, it is confounded by other factors that are much stronger.
The hypothesis itself is perfectly reasonable. But you can’t discover the association and test it with the same data set, certainly not in a snipe hunt among 28 snipes with p set at 0.05. certainly not when the differentiation is small changes in a well-known probability.
I mean, which is it? Did the heat wave make the probability 0.0006 (a 50% increase) compared to the control population and was the control population still at 0.0003-whatever (since AFAIK it is only known to be “between 0.0003 and 0.0004” in the US in general)? The smaller subgroups they are trying to analyze have an even smaller representative population. Not only were the jellybeans green, but they worked the best on people believed to have acne that is sensitive to sugar.
I wonder why the blue ones don’t?
rgb

Sorry. I am 67 and don’t have the time to go through 110 comments so if anyone has already made this point, ignore me!
I believe the correct way to procede would be to say:
“There is no real evidence for a link between 27 out of the 28 conditions hence we can ignore those and look at the only 1 which has triggered what might be a significant link. Two questions come up. Is there a causal link between temperature and the condition we are studying? Common sense suggests there isn’t (based on the Phoenix/NY arguement). However, if you are really sure this is a critically important problem, do the test again using other data from another place. Surely there are plenty of big cities which have an increase in temperature (UHI effect) and equally surely there are other sets of health studies. If you find that of the 20 sets only 1 does NOT show a significant correlation then you have some kind of evidence that there is a genuine (i.e. P=0.05) correlation.”
THEN you have something to publish and get your fame and glory. But only, of course, if you can find a causal link between temperature and your condition.
For example,there is a strong correlation between the temperature in London and the percentage of men in Germany wearing coats. Does this mean that wearing overcoats in Germany is caused by the temperature in the London streets? Or could it just be that the common causative factor is winter in the Northern hemisphere?

Meanwhile, back at the UN ranch.
This would have been the second surprise. Throughout the epidemic, science has been the last thing the UN’s political leaders have wanted to talk about.
http://www.winnipegfreepress.com/opinion/westview/UN-fakes-effort-to-help-Haiti-184151891.html

From the Readings section of Harper’s magazine, January 2013 issue.
The following comes from the instruction manual of a board game called “The Settlers of Catan” Developed by the Worldwatch Institute and the game’s manufacturers.
During your turn, you can convert one oil into two non-oil resources of your choosing. Alternatively, you may choose to forgo the usage of oil, sacrificing some growth for increased environmental security and the prestige of being a sustainability leader. The first player to have sequestered three oils gains the “Champion of the Environment” token.
For every five oils used, an environmental disaster results. Roll the two six-sided dice to determine where disaster strikes. If a seven is rolled, a natural disaster triggered by climate change floods the coasts. Settlements bordering a sea are removed from the board, and cities are reduced to settlements. Roads are not affected. A metropolis (because of its seawalls and other advanced design) is also not affected. If any other number is rolled, industrial pollution has struck. If the affected hex does not contain an oil spring, remove the production-number token from the hex. That hex no longer produces resources.
If the fifth number token is removed from one of the hexes, flooding has overwhelmed Catan and all inhabitants are forced to abandon the island, thus ending the game. While no player truly wins, the player who currently holds the Champion of the Environment token is recognized by the international community for his/her efforts to mitigate climate change and is granted the most attractive land on a neighboring island to resettle.

@michaeljmcfadden
“Rg, I’ve always had trouble with that Deal paradox. Say I had a thousand doors and 999 of them had nothing behind them and one had a zillion samolians. I pick one and clearly have one chance in a thousand of winning. Monty Hall then randomly opens up 998 of them and they all display nothing. The chances at that point of the door on my left (which I had picked originally) having the zillion should be, as far as I can intuitively be comfortable with, 50%, i.e. the same as for the other door. And yet the Monty Hall “explanation” *seems* to tell me I should switch my choices to the door on my right.
Somehow that has NEVER sat comfortably in my gut. It simply feels like a joke being played by statisticians to see how gullible people can be.”
You are incorrect and the Monty Hall expanation is correct. This has been tested by Mythbusters.
Look at it this way. For any N door contest with only one prize the probability that the door you select does not contain the prize is N-1/N.
When the host is opening empty doors he knows which door has the prize and won’t open that door. Because of this it doesn’t matter how many doors are opened, the odds you selected the wrong door don’t change and all the extra probability goes to the remaining unselected door.
You will always have better odds switching, the greater N is the greater the advantage to switching.

Long story short —

rgbatduke says:
December 20, 2012 at 1:18 pm
“…
Pity the poor suckers like Michael Mann who actually wrote a PCA package in fortran so he could do his tree-ring work. Along comes M&M who actually use, um, R … and show that his results are bullshit with a few well chosen simulations and exercises.
…”

Put nicely that is.

willis and rgbatduke make for a helluva one-two punch.

I don’t remember if it was the WSJ at the moment, but your story reminds me of a study where the researchers got headlined as showing that exposure to a certain substance caused “hypertension” in “boys as young as eight years old.” The “hypertension” was an increase of roughly 1 unit systolic, and the pool of boys was actually a range of 8 to 17 years old, but the headlines didn’t go into that sort of detail of course.
The stories weren’t much better, although one of them *did* mention that the deadly exposure happened to DEcrease systolic readings by almost TWO points among girls. The researcher was asked about that and noted that decreased blood pressures could also be a health threat.
No, I’m not kidding. I can dig out the refs if anyone wants.
– MJM

Not been on this before, but what a great range of comments in this thread. One of the really nice things was the humility of rgbatduke – someone who clearly is comfortable with the use of statistics, remarking on what he does not know. It’s sad to note though that folks who don’t come anywhere near his level of expertise are able to pass themselves off as statistical experts, and so our society is being shaped in so many ways by those who abuse their level of knowledge – the AGW scam, passive smoking, efficacy of drugs, you name it, our society is being shaped by those who are abusing their knowledge in some way. I’ve gone through university in the UK – did various degree courses (biochemistry, public health engineering) in which statistics was always a part. But I never paid any attention to the stats part of the courses, simply because in my final exams, stats would usually get maybe one question out of 8, and I could rely on my choice being reduced to answering 5 out of 7. So I always ignored stats – until I did an MBA, there was no getting past it, I finally had to knuckle down. What I remember from that is enough to know that usually I don’t know – so I’d like to think I’m in the group who know enough to know what I know but more importantly, know what I don’t know. As the general population is made up of people who don’t know that they don’t know stats can be abused without fear of comment. And our society is very much poorer for it. Environmental groups can frighten folks with claims that are simply preposterous – and few ever question them. They are the new gods of information, held in high esteem by the general population all over the world, yet they are mainly charlatans, selling quack medicines. With the dumbing down of education here in the UK, things will only get worse. I cannot see any change at all – not until university courses are organised so that stats is an integral part of every question – no escape; and basic stats is taught as a “must do” subject in every school. Probably will never happen. So good luck guys, in your quest as the voices of reason and truth, but I fear you are both literally and figuratively tilting at windmills.

Monty’s not opening the doors randomly. He’s opening them because he knows they don’t contain the zillion samolians. Each empty door he opens gives you a little more information than you had when you made your original choice. If he were opening them randomly, your gut would be right.
And Bayes theorem and information theory are one and the same thing, in the end. Information is the key. Monty can actually open any of the doors that (he knows) do not contain the prize randomly (if there are more than two), it won’t matter. But he cannot open a random door out of the set of unchosen doors, because then it might contain the prize. We learn a bit more of what Monty knows with every door he opens, and it shifts our odds if we use the information. But this is hard to understand, so let’s work it out. It’s not too difficult.
Here’s a very simple parsing of the chances. When you chose between the 3 doors originally, you had a 1 in 3 chance of guessing the right door. Each of the other doors also had a 1 in 3 chance of being right, so 2 out of 3 times you guessed wrong. Monty opens a door and reveals it to contain a year’s supply of dogfood — and a dog — not the two week vacation in Tahiti. The door you selected has a 33% chance of being right, but the other door has a 100% chance (now) of being right — 67% of the time.
Better to switch, don’t you think? Unless you cherish the barbecue grill and year’s supply of steaks that is the other “junk” prize.
The key thing is to realize that the second “trial” is not independent of the first, so you have to deal with conditional and joint probabilities, which is what Bayes theorem is all about.
Note well that this is defeated if Monty is permitted to choose whether or not to open a door. For example, he could open a door only if you’ve chosen the actual prize to entice you to switch — this is actually not crazy, maybe the company likes to conserve valuable prizes. So if he knows you have chosen correctly, he opens a door to entice you to re-choose (assuming that you are a good statistician and know that you “should” double your chances by doing so, but if you have chosen incorrectly he always goes ahead and reveals the dogfood you’ve chosen. Ouch. You win only if you always stick, 1/3 of the time, and even less if you are enticed into switching — you win 0% of the time if you always switch.
This opens up a whole range of games Monty can play by rarely opening a door when the prize is on his side even if you’ve lost, so that you learn that there is some chance of winning. Most of those games (if played perfectly) would increase your optimum yield from 1/3, but now we’re well on the way to inventing rock-paper-scissors games, where the optimum strategy is to guess completely randomly (and win on average half the time) but where humans can often nevertheless exploit information theory and beat the hell out of their opponents when neither side chooses randomly. Humans have a hard time CHOOSING randomly without using dice or coin flips — what we intuitively think of as being a “random” sequence almost never is.
rgb

It’s seems a strange conclusion, even without dredging the data for correlations. These were *unborn* kids supposedly affected. During their gestation, where they were was thermostatted far more precisely than any conventional environmental control system. That’s the advantage of being internally gestating homeotherms.
Or are there Burroughs’ Martians among us?
It isn’t that simple. There is substantial, convincing evidence with p much smaller than 0.05 that fevers during critical periods are correlated with the defect, where it is more likely the fever per se that is the cause and not the virus or other cause of fever because it is positively associated with the fever caused by MANY diseases, not just one or two.
It is also certainly a true fact that prolonged exposure to very high outside temperatures with inadequate shade, water, and/or too much humidity so sweating doesn’t work to keep you cool can lead to hyperthermia, where your internal body temperature creeps up over its “normal” set point because you literally cannot throw off heat fast enough. When the temperature outside is 100F, for example, you are trying to cool into an environment hotter than you are, which REQUIRES sweating profusely. One can literally overwhelm one’s natural thermostat, leading to heat exhaustion and heat stroke as your body’s internal temperature rises to 102F and then to 104-105 F, where your brain basically malfunctions and you have strong odds of straight up dying.
It is therefore a reasonable hypothesis that environmental hyperthermia during pregnancy could lead to birth defects, this particular one among others. As I pointed out, neural tube defects are the most likely ones to first observe as they are most strongly correlated with fever-induced hyperthermia.
However, this is a long ways away from showing that pregnant women have a high enough chance of experiencing actual internal hyperthermia to affect birth defect rates by increasing them by 50%. That is, in fact, close to absurd. It takes time and a certain amount of abuse or stupidity to experience hyperthermia, as we naturally seek cooler environments and cool drinks when it is hot out. One needs MOST of the women in question to have spent long times outdoors, not drinking, and consequently overheating to the point of having the equivalent of a low grade fever. Maybe, but I doubt it, and if so why weren’t neural tube defects spiked first?
Bayesian analysis of neural tube defects and other disorders related to fevers suggest that the hypothesis is probably false, and in any event is marginal on the data, especially when it is a dredged conclusion.
rgb

The most common cause of cataracts in infants is being kissed on the eyes by a parent with a herpes outbreak or “cold sore” on their lips. Source: “Herpes: What to do when you have it” by Dr Oscar Gillespie

One nit — The 75% chance is for any study that has 28 different data dredges going on. There is not a 3/4 chance that any one of them will be.
If 10 different studies of this sort were done, 3 out of 4 would get a positive result.

http://imgs.xkcd.com/comics/depth_perception.png

I’d say that a statistical study on statistics would probably show that, as a statistical average, people are not very good at statistics.