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Freeper heads up: This is a very long article that I think is a must read(but what do I know?). You may want to flag it for later reading.
Several studies indicate that power lines cause cancer, while others indicate they do not. One study links video display terminals to miscarriages, but several others do not. A man claims his wife got her brain cancer from a cellular phone. But some medical authorities say that's crazy. As Meryl Streep asked during the Alar scare: "What's a mother to do?"
Well, there is a method which seeks to sort out this madness. It's called epidemiology.
Epidemiology is a science of association, relying on statistics plus knowledge of how illnesses or accidents come about (which is known as etiology). The purpose is to detect what is causing the problem and how great the problem is, in order ultimately to reduce or eliminate its incidence. Epidemiology is based on observation, and is thus in contrast with laboratory studies, which develop hard cause-and-effect relationships from experimental evidence.
An epidemiological exposure study usually has three parts. First, it isolates a group that has been exposed to a particular substance or other possible cause of illness. Then, it determines if the group has been more prone to a particular illness or injury than the rest of the population. Finally, if there is an excess incidence of illness or injury, it tries to decide, by excluding all other possible factors, whether the excess is a result of exposure to the substance in question.
Lab experiments don't always produce expected results.
Most such studies are inconclusive and it may take many years to find a cause-and-effect relationship even when it turns out there was a particularly strong one, such as that linking lung cancer and cigarette smoking. Thus, epidemiology can be a crude tool, although when it does work its results are far more reliable than those of studies in test tubes and on lab animals, because those factors that cause certain effects in the laboratory or in a rodent or dog will not necessarily produce the same effects in a human.
This article takes a look at the basic rules of epidemiology. Some of those rules look really basic, even simple, but you'd be surprised at the trouble some people have with them.
Tenet 1: Everyone dies. Death takes a holiday only in the movies. That seems simple enough, but many people will, in the heat of the moment, forget it. The chances are that if your local newspaper ran a story saying, "Since the hazardous waste incinerator began operating last May, 186 people have died," there would be panic – if not in the streets then somewhere. A lot of people wouldn't stop to think about how many people would have died in that same period, regardless of the operations of the incinerator.
A point related to this is that while we average a little over 70 years of life apiece, that is indeed simply an average. Some of us survive to 105; others die in infancy. Cancer and heart disease tend to be diseases of the elderly, but often a young person will die of cancer and occasionally one dies of heart disease as well. That is not fair, but then neither is life fair. So not only do we have to keep in mind that everyone dies, we must also remember that a lot of us die prematurely.
Tenet 2: One fourth of us will contract cancer and one fifth of us will die of it. Indeed, as the population ages and fewer and fewer people die of other causes, more and more will die of cancer. Why? Because you have to die of something. (See Tenet 1.) Cancer, for the most part, is a disease of old age, and the nation that has reduced its incidence of diseases killing younger people will find its cancer rates increasing.
Tenet 3: The mechanics of how cancer develops are not well understood. Damage to the DNA of cells and improper cell division are thought to be at the root of the formation of cancer, but beyond that, our understanding of the disease begins to get quite foggy. DNA damage to cells and improper cell division don't always lead to cancer and, further, we often don't know what causes such damage or improper division.
Tenet 4: Most cancers are unexplained. That follows from Tenet 3. Since we don't know exactly how cancer comes about in general, it is obviously difficult to say how it came about specifically. Again, this is a concept with which many people have a great deal of difficulty.
Most lay people probably don't understand that in all cases it is impossible to say for sure how someone got cancer. If a person who contracts lung cancer smoked three packs of cigarettes a day for sixty years, it's a good bet that the cancer resulted from cigarette smoking, but that is by no means certain. Consider the number of heavy smokers who die in old age of diseases having nothing to do with smoking; and consider that about 15% of lung cancer victims have never smoked.
Tumors do not come stamped with an identification of their cause. A lump in a breast caused by consumption of alcohol looks exactly like a lump caused by excess exposure to radiation. A brain tumor caused by exposure to plutonium would look exactly like a brain tumor that occurs for no reason that can be identified.
One exception to this is mesothelioma, a lung disease which appears to be almost exclusively associated with asbestos exposure, but even here there appear to be some cases where asbestos is not the cause. If you read that a doctor proclaimed that X cancer was caused by Y, you may assume that either the doctor is wrongly treating his opinion as a fact, or that the media have wrongly interpreted what the doctor said.
Thus, when a person says, "I got cancer from working at such and such plant, or because I lived too close to such and such factory or because I ate such and such," he cannot possibly know for sure, nor can his doctor, nor can the wisest diagnostic physician on the face of earth. Which leads to Tenet 5.
Tenet 5: Being a victim of a disease does not make one an expert in how that disease is contracted. It is a curious phenomenon that one afflicted with a disease is often treated as an expert solely because of that affliction. A man who claims he got cancer from working at a certain job is automatically given great credence.
In 1991, the late football great Lyle Alzado made the news by declaring that his inoperable and ultimately fatal brain cancer was caused by anabolic steroids. "I used a certain steroid that caused me to ruin my immune system, " he said. He added, "I just hope that this interview ... will convince the other people – junior high school, high school and college students – that they can do without this stuff."
In fact, it is documented that taking steroids can do all sorts of nasty things to the body, though brain cancer is not one of them. Further, people have been dying from brain cancer long before anyone ever had access to steroids that could be ingested or injected. Thus, neither Alzado nor anyone else could possibly say with authority that his cancer was linked to steroid use.
But the Associated Press, Sports Illustrated, Cable News Network, and at least one nationally syndicated columnist carried this story without any suggestion of this discrepancy.
Alzado said he got his cancer from steroid use, so who are we to argue with him? Besides, his story could scare kids out of using steroids. True, but it is still bad science. In fact, a brain cancer is a brain cancer, whether it was caused by cigarette smoking, air pollution, or by some sort of spontaneous cell mutation of which we understand nothing. The sufferer of that cancer has expertise in what it's like to suffer from that type of cancer, but nothing more.
The victim's assertions may be valid in that they give insight into his feelings. That may be of interest to the average newspaper, and to the average reader, but not to the epidemiologist. Yet time and again we read stories in the press or see news shows on television in which a cancer victim is stating that he or she knows that the cancer came from exposure to a nuclear power plant.
Likewise, we will occasionally read the story about the woman who "knows" that her child got cancer from exposure to a toxic waste dump or a pesticide. This is not science; it is superstition.
Tenet 6: The physician treating the victim does not necessarily have any expertise in the cause of the problem when that problem is cancer. If it makes no sense to treat a victim of a disease as an expert in epidemiology, it also does not make much sense automatically to attribute such expertise to the treating physician. The media seem generally to assume that anyone with an "M.D." after his or her name who is willing to speak on a given medical subject is an expert in that subject.
In fact, most doctors who work outside of epidemiologically related areas (which includes the physicians you see when you are ill or hurt) took a couple of epidemiology courses way back when and now know about as much about epidemiology as you know about chemistry because you were required to take it in high school back in 1969. Further, the practice of a treating physician would not put him or her in a position to study epidemiological patterns. That is, looking at individual cases is of little use in getting the big picture.
Thus, a treating physician may make a statement like "I've never seen another case of this disease in a man of this age." The doctor may think this has great epidemiological importance, as will a reporter, as will then the reader. In fact, it probably has none. If he suddenly sees five such cases, that might mean something. But that he has practiced for many years and this is his first case means nothing.
Tenet 7: Miscarriages are common. Studies have shown that rates for miscarriages after a recognized pregnancy vary from about 12.5% to 33.9%. Some of the earlier studies showing the higher end probably suffered from various errors, so a risk of about 12% to 15% seems most likely. As for the total rate of pregnancy lost after fertilization, including those that a woman couldn't ordinarily have recognized as even having been a pregnancy, one recent study put this figure at 31%. These are higher rates than some of us probably would have thought, the point being that when you look at a number of miscarriages in an office or a neighborhood and the figure seems high to you, it doesn't mean that it really is high relative to the expected number.
Obviously these are generalized numbers. Some categories of women have a much higher risk (those over 35 for example, or those with untreated severe diabetes), while others have a lower one. A good epidemiological study doesn't compare miscarriages in a given group with the national rate of miscarriages; rather it tries to match up similar women (a control group) who do not have the risk factor being investigated but have much in common with those who do.
Tenet 8: Birth defects are common. Probably about 2% to 3% of all babies born in this country exited the womb with at least one major malformation. Since about four million babies are born annually, that is between 80,000 and 120,000 babies born annually with birth defects.
Tenet 9: Most miscarriages and most birth defects are unexplained. One recent study found that, 43% of 1,549 miscarriages studied had a completely unknown cause. Of the other 57%, most of those were also to some extent of unknown origin. For example, it could be said that many of them were related to chromosomal abnormalities in miscarried children, but it is often difficult to tell what the origin of those chromosomal abnormalities is.
The reason for these unknowns parallels the explanation of why we don't know the origin of most cancers. There just isn't enough knowledge about what causes birth defects in the first place.
Dr. Lewis Holmes, author of the aforementioned birth-defect study, says: "This bedevils us as much as it does the victims. Most people have blamed themselves, neighbors have given them ideas. They don't understand that even genetic disorders often come as a total surprise."
Further, Holmes notes, even the technology that has been around for 30 years that sometimes can determine causes of defects and miscarriages is not utilized. So when a woman's family physician or gynecologist tells her, "I just don't know why you had this problem," it may simply mean that he doesn't know.
Tenet 10: Epidemiology is a complex science. Setting up a proper epidemiological study is extremely difficult even when done by top professionals. Compare epidemiological studies to quizzes given in school. In any class, there will be some students who will do well on all quizzes and some who will do poorly on all quizzes, but most will have a range that, taken as a whole, will probably give a professor a good idea of the student's ability in that class. Even this assumes that the professor's quizzes are fair evaluations. In any case, any given quiz or epidemiological study does not have much weight without the support of others.
That's why it's not unusual to hear on Monday that coffee has been linked to cancer and on Thursday that there is no link, to hear on Tuesday that birth control pills cause heart disease and to hear on Friday that they do not. There is not necessarily dishonesty or a cover-up involved; there are just so many problems that must be factored out. It can take years, even decades, to do so. That is why it is simply wrong for a scientist or, as is far more often the case, a journalist or other public crusader to build a whole case around a single study, or even around two or three.
What a good journalist can do is to poke holes in a bad epidemiological study. That is the essential equivalent of a nonarchitect walking into a house and seeing that it was very poorly designed, or a landlubber noting that the ship she's on is listing badly. But the building and piloting of these houses and ships is a job best left to the professionals.
Tenet 11: Epidemiology is an inexact science. Epidemiology cannot detect all causes of illness. If an illness is fairly common, a slight increase as caused by a specific agent may be impossible to detect. Thus, for example, Frederick J. Stare, Robert E. Olson, and Elizabeth M. Whelan, all of the American Council on Science and Health, write in their book, Balanced Nutrition: "Alar has been used since 1967 without a single case of cancer or any other disease attributed to its consumption at approved trace levels in apples."
But of course not. Almost all of us have at one time or another consumed apples and one fourth of us will get cancer. Searching among those tens of millions of cancers for those caused by Alar is like trying to determine if someone threw a brick into the backyard swimming pool by measuring the water line. Alar could cause 5,000 cancers a year, but against the backdrop of a million cancer diagnoses a year among apple eaters, you would never know it.
On the other hand, a brick thrown into a kitchen sink would cause a perceptible increase in the water line. The equivalent to this would be to measure those with extraordinary exposures, for example, workers who were exposed to high levels of Alar. Unfortunately no such study has ever been made.
Tenet 12: Epidemiologists are only human. They can make mistakes that overstate or understate a problem. They may, very rarely to be sure, intentionally skew their data to meet predetermined conclusions. On the whole, epidemiologists are more trustworthy by a factor of at least ten than the journalists who will relay their work to the public or than the regulators or politicians who will pass regulations on the basis of their studies. Epidemiologists on the whole are also quite careful and conservative in their language. If they observe, for example, twice as many influenza cases one week as the one before, they might say that this development was significant." And if half the town got wiped out by bubonic plague, that too would be deemed "significant." Not "very significant," or "extremely significant" or "utterly horrifying," just "significant."
Tenet 13: Associations do not equal cause and effect. Just because people with some ailment in common have another thing in common doesn't mean that the other thing caused the ailment. When I was very small, I observed that most folks drinking diet sodas were overweight. I therefore hypothesized that gulping Tab, which was just about the only diet soda there was then, made one fat. Likewise, to sit in the sauna of any American health club is to come to the conclusion that there are a disproportionate number of heavy people in these rooms. It is probably not wise to conclude from this that saunas make you fat, any more than does Tab.
Tenet 14: Rare diseases still have to happen to somebody. If you read that "John, who worked for twenty years at a radioactive widget factory, was suddenly stricken with an extremely rare form of cancer, that of the little toe," your first thought is probably that the cancer has something to do with the radioactive widgets. But assuming that 100 men and women each year get this very distressing form of cancer of "this little piggee," it must be asked, what of the exposures of the other 99?
In one highly publicized incident, a couple's twelve-year-old was diagnosed with osteosarcoma (bone cancer), and since they lived near a plutonium-processing plant and osteosarcomas strike only 520 American children a year, the parents assumed the plant caused the cancer. The local media seemed to share that assumption. What they didn't take into account was that 519 other Americans got that cancer in that year alone, none of whom lived anywhere near the plant.
Tenet 15. "Clusters" almost always mean absolutely nothing. Strictly speaking, a cluster is simply an elevated incidence of disease or other problem in a given population. Cancer, birth defects, and miscarriages are the three subjects that most often come up in media accounts of clusters.
Consider a group of sixty people of whom we would expect 20%, or 12 in number, to have cataracts. By great coincidence, they turn out to have exactly the correct percentage. We line them up in three rows in alphabetical order. They are represented below, with Ns being normal and Cs representing cataracts.
CNNNNNNNCNNNCNNNNNNC
NCNNNNNNNNNNNNNNNNNN
CNCCNNNCNCNNNCNCNNNN
Since they have exactly the percentage of cataracts we would expect in this group, when we look at all three rows together, we find nothing unusual. No clusters. But if we look at them row by row, suddenly we find that cataracts are heavily overrepresented in the third row. Cluster! Of course, we know this means nothing, since the place one's name occupies in the alphabet is not considered a risk factor for developing cataracts.
There are virtually an infinite number of ways of breaking down groups--sex, race, address, occupation, age categories, and so on, plus combinations of these categories. If you are looking for a cluster, you will always find one, simply by arranging arbitrary categories. You may find that black females living on the north end of town working as clerk typists who are between the ages of 15 and 30 have no elevation of cancers. But if your sample group of women with cancer has two such women who happen to be 31, by tossing them in suddenly you may have doubled the expected rate of cancer. Call in the news crew! Obviously a good epidemiologist tries to avoid such arbitrary breakdowns of groups, but most reporters and citizens in general don't know the first thing about such methodology.
So what good, then, are clusters? In the hands of amateur sleuths and crusaders, such as those whose reports fill our magazines, newspapers, and airwaves, they are no good at all in epidemiological terms, and very harmful in sociological terms – in other words, they serve no purpose other than scaring the hell out of people.
What good are clusters in the hands of trained professionals, then? Not much there either, actually. At the National Conference on Clustering of Health Events, sponsored by the CDC in Atlanta in 1989, keynote speaker Kenneth J. Rothman, editor of the journal Epidemiology, argued that "with few exceptions, there is little scientific or public health reason to investigate individual clusters at all." Such efforts, he said, are increasingly becoming "exercises in public relations," fueled by health-conscious consumers and public misperceptions that research is the answer to every problem.
Rothman said: "If the epidemic of cluster research continues, it will eventually intrude on more productive epidemiological investigation of environmental exposures."
Alan Bender, chief of the Minnesota Department of Health's Chronic Disease and Environmental Epidemiology section, said at the conference that of 500 reports of suspected clusters in Minnesota, only 5 prompted enough concern for formal studies. Other states reported similar rates. For example, from 1961 to 1983 the CDC investigated 108 cancer clusters from 29 states and five foreign countries. It found no clear cause for any of the clusters.
While clusters are of little use to epidemiologists, they are a wonderful tool for crusaders seeking to indict something as a cause of disease. Tell a layman that a given office building or a given city block has had twice the cancer victims or heart attack victims as the expected rate, and he instantly assumes that something is wrong in that building or on that block.
The concept of epidemiology should be clearer to the reader now, and yet it is to be hoped that something else is clear to the reader as well. While the concepts of epidemiology are basic, the application is fraught with pitfalls. The journalist or other layperson who fancies that he can just look at a cluster of cancers or other disease and say, "Aha! There's clearly a problem there!" or who goes even further to say, "And I know what's causing the problem! " is guilty of taking the surgeon's tool unto himself and cutting away. The journalist who, upon finding that the epidemiologists disagree with him, insists that they must be engaging in a cover-up, is not only grossly ignorant, but arrogant as well.
As with any profession, epidemiologists have developed their own lingo, some of which is good for lay persons to know, some of which is better ignored, being essentially the equivalent of "legalese." Epidemiologists express the mathematical possibility of increased risk by using risk ratios.
A risk ratio or odds ratio of 3.0 for lung cancer means that three times as many people showed up in that category with lung cancer as in the control group. A risk ratio of 4.2 for leukemia means that 4.2 times more cases appeared than in the control group. A control group is a set of persons carefully matched to the set of persons who are being observed for the problem. Thus, an epidemiological study of women using video display terminals should have as its control women who didn't use VDTs but who did as much sitting and as much smoking as women who did use VDTs.
Nevertheless, it is not cut-and-dried that risk ratios above 1.0 mean that something special is causing the cancer or other ailment being looked at. That is because of the laws of chance and probability. Thus, if you flipped a coin four times, you might expect two heads and two tails. In fact, it often doesn't work out that way. Often you'll get three of one and one of the other. That would give you a "risk ratio" of 1.5, because you are getting 1.5 times the number of heads or tails that you expected. It doesn't mean anything is affecting the coin; it's just chance. Diseases often cluster just by chance... (See above, Tenet 15.)
To augment their risk ratios, epidemiologists use "confidence intervals." Thus, you might see a risk ratio expressed as "2.9 (0.9-3.5)." This means that, expressed in strictest terms, the risk ratio is 2.9 but anything between 0.9 and 3.5 is within the range of the results of the study. In fact, even this parameter is not that solid. Confidence intervals themselves may be off. Thus, epidemiologists will say, "This is a 95% confidence interval," meaning that there is a 5% chance that even this broad range is inaccurate. A 90% confidence interval means that there is a 10% chance it is wrong, and so on. If it were conducted incorrectly, the results are completely thrown off.
At any rate, when not only the relative risk number is above 1.0 (the term "elevated" will often be used to describe this) but so is the bottom of the confidence interval range, then epidemiologists say that number is "statistically significant." It is very important to grasp this simple concept. A risk level elevated at 4.0 may look very serious. It says that four times as many cases of such-and-such are showing up as in the group that wasn't exposed to the suspect agent. But if few enough people are involved in the study, the confidence interval may be something like 0.8-9.0, indicating that the elevated risk level of 4.0 may mean nothing other than that's how the coin landed. The more people that are involved in a study, the closer the confidence interval and the better the chance that an elevated risk level actually means something
bump for later.
Thanks,
L
Thanks for this. I have printed it and will read it on the bus going home tonight. (If I'm not killed in a traffic accident.)
I find it useful to break down research into three categories:
Thanks. Everything Fumento writes (or has ever written) is worth a read. From AIDS, to weight loss, to environmental science. He, more than any other author, public figure, or anyone else I can consider, turned me to the path of conservative thinking (i.e. being suspicious of the motives of authority, particualrly government.)
By using the scientific method tools I had studiously learned during my academic education, to prove that I had been misled and lied to regularly by politicians, activists, and the media, he led me never to blindly trust them again, unless I could verify for myself.
Thanks, Michael!
you seem to be knowledgeable on the subject...What are your feelings on the media picking up studies that were put out with the intent of peer review and running with them as if they were conclusive?
It is better to die with knowledge in hand, than to die not knowing.
Seems pretty deep, huh?
One quibble, if I might - clustering can be invaluable if there are enough victims in the cluster. Take for example the British doctor (I've forgotten his name) who was faced with a cholera breakout in London in the late 19th century. He mapped the cluster and discovered that it seemed to be centered around a specific well. Which turned out to be infected.
That said, Fumento is mostly correct about the current uses to which clustering is put. A "cluster" of 10 cancer cases in a population of 100,000 is not really a cluster at all.
bump
Excellent! I've tried to make many of these same points on these threads.
Bookmarked for easy access.
Thanks. Fumento has long been a favorite of mine.
Well worth posting (and printing to save for later). 'Erin Brockovich' comes immediately to mind as a recent example of getting a big settlement from a company based on amateur epidemiology. I can't vouch for whether there was underlying fault on the companies part or not, but it looked like a situation where emotion had as much to do with the outcome as cause-and-effect.
I especially like the following point: "Rare diseases still have to happen to somebody. If you read that "John, who worked for twenty years at a radioactive widget factory, was suddenly stricken with an extremely rare form of cancer, that of the little toe," your first thought is probably that the cancer has something to do with the radioactive widgets. But assuming that 100 men and women each year get this very distressing form of cancer of "this little piggee," it must be asked, what of the exposures of the other 99?"
To illustrate the natural impulse behind the fallacy with a trivial example, my wife is always marveling that this or that famous person "came from that small town". My reply is always, "everybody was born somewhere" (although I actually believe that important people are more apt to come from small towns than not - you can of course guess the size of the town in which I was born).
proven the Fibonacci conjecture
What in Heaven's name is that? If it involves more than simple calculus just tell me it's over my head and I won't be insulted.
Isn't cholera bacterial? If so then of course a cluster would be evident as it would be centered around the source of the bacteria. I think this has more to do with hygiene and sterilization than with clusters. Of course I'm wrong if it is not bacterial.
That was Dr. Snow, the father of modern epidemiology.
if you go to Fumento.com he has several pieces on Erin Brochovich
What are your feelings on the media picking up studies that were put out with the intent of peer review and running with them as if they were conclusive?
With enough "research studies" being performed, there will some that say practically anything. For the media to report the results of studies they "like" while ignoring those they "dislike" is really not much different from the media simply stating its own viewpoint, except that by quoting "studies" they make themselves sound more knowlegeable.
proven the Fibonacci conjecture
What in Heaven's name is that? If it involves more than simple calculus just tell me it's over my head and I won't be insulted. It involves the effects of neurons misfiring and causing one to momentarily get confused about mathemeticians whose names start with "F". [I meant to refer to the Fermat conjecture, which should be much more familiar.]
Bacterial it is: Vibrio comma, now Vibrio cholerae. And many thanks to Eagle Eye - Dr. Snow was the young (then) fellow's name. My memory's not so good since college...I'm thinking it's those durn cellphones...I feel a tumor coming on...
But what about the idea of the news running things on the Health Beat about how Gerbils can cause Parkisnson's disease when there has been one or two epidemiological studies on it showing a result that can be deemed significant, but not so much so to be conclusive
I dunno about gerbils. I just know that research causes cancer in lab rats.
flag!
You may be interested
This is an excellent article.
There was a funny case here. Some artist (self-styled activist) took the five years with the highest incidence of brain cancer than asked why the number of cancers was larger than the national average.
How about the school board member who lamented that 'fully half' the kids were below average!? That was in the adjacent county.
What is the Fibonacci conjecture?
I don't follow you.
Thanks for this post. Its theme is the sub-text of so many conflicts today: The struggle between "democracy" and "science"--two concepts so at odds and yet so conflated that the definition of "science" is literally at stake. In fact, with government funding of so much "science" at the unitversity level, it could be argued that science has capitulated.
Maybe it capitualed from the start. Which would explain why military expenditure has been responsible for some of the greatest advancements in science.
I love Fumento. Check out his "hate mail"..hilarious!
I'm sure some Freepers are going to be VERY VERY upset when they read his "Gulf War Syndrome" writings...
Only Fumento and that (flaming liberal, interestingly) Dr. Dean Edell have had the guts to say in public that they think Gulf War Syndrome doesn't exist..
I'm sure Fumento will have some interesting things to say about depleted uranium in the Balkans soon...
I love Fumento. Check out his "hate mail"..hilarious!
I'm sure some Freepers are going to be VERY VERY upset when they read his "Gulf War Syndrome" writings...
Only Fumento and that (flaming liberal, interestingly) Dr. Dean Edell have had the guts to say in public that they think Gulf War Syndrome doesn't exist..
I'm sure Fumento will have some interesting things to say about depleted uranium in the Balkans soon...
bold off
The best thing about Fumento is that he's a Freeper! His screen name is "mfumento".
We were discussing the Erin Brockovich story last spring and I was getting jumped by all the "all corporations are evil" and "I saw the movie, so it has to be true" posters that tend to pop up when stories like this are posted. Anyway, he posted some information to back up his story. I even e-mailed him to thank him, and we ended up having a nice discussion.
A very nice man.
Check out www.junkscience.com
If one picks the five members of a professional basketball team, they will be tall, not because people are tall in general, but because basketball players are selected to be tall. This artist picked the five highest years, (not five random years) and asked why they were high. They were high because the guy picked the big one; it's not a random sample.
Bad Bold, go away...
Again...
flag! (what the hay?)
have you read this yet?
I had read it - but it never hurts to have a re-read - and it could stand to BUMPED again!!!
Thanks for the reminder.
FYI
bump to self
This sort of article needs to be run in major media outlets on a regular basis. The essence of what is stated is good information. I would add something about relative risks of behavior and environmental dangers, but I would definitely leave out the following nonsense:
What a good journalist can do is to poke holes in a bad epidemiological study. That is the essential equivalent of a nonarchitect walking into a house and seeing that it was very poorly designed, or a landlubber noting that the ship she's on is listing badly. But the building and piloting of these houses and ships is a job best left to the professionals.
Really, if an epidemiology study is tilting to such a degree that it's visible to a journalist, I would wonder how it could be funded or published at all. Should journalists be arbiters of what is 'good' or 'bad' science?
Epidemiologists on the whole are also quite careful and conservative in their language. If they observe, for example, twice as many influenza cases one week as the one before, they might say that this development was significant." And if half the town got wiped out by bubonic plague, that too would be deemed "significant." Not "very significant," or "extremely significant" or "utterly horrifying," just "significant."
The word "significant" is a technical term; it's use has nothing to do with epidemiologists being conservative in their language.
That would give you a "risk ratio" of 1.5, because you are getting 1.5 times the number of heads or tails that you expected.
Risk ratios compare two groups(cohorts/populations/subpopulations). It's imprecise and misleading to say "(the number) that you expected", unless you are a Baysean.
Thus, epidemiologists will say, "This is a 95% confidence interval," meaning that there is a 5% chance that even this broad range is inaccurate. A 90% confidence interval means that there is a 10% chance it is wrong, and so on. If it were conducted incorrectly, the results are completely thrown off.
This is not precisely correct (he should say likelihood rather than chance, since there is no chance involved - the parameter either falls within the CI or it doesn't). However, it's the best definition I've seen in pop writing.
A risk level elevated at 4.0 may look very serious. It says that four times as many cases of such-and-such are showing up as in the group that wasn't exposed to the suspect agent. But if few enough people are involved in the study, the confidence interval may be something like 0.8-9.0, indicating that the elevated risk level of 4.0 may mean nothing other than that's how the coin landed.
Misleading. The 95% standard for significance is arbitrary, which is why results are usually reported by p-values (p=.07 means that a 93% standard for significance would have been met.)
The more people that are involved in a study, the closer the confidence interval and the better the chance that an elevated risk level actually means something
Again, somewhat misleading. The confidence interval is not solely determined by the sample size; the sample variance (std. error) and the uniformity of the results (in pairwise analyses)are also factors. Dose dependence (e.g., the more tofu someone eats, the more brain damage they get) can also be an important indicator of a causal relationship.
Overall, this is an excellent article.
I don't know...I've seen some studies that had serious flaws in them and I am not even a journalist. I think the point he is making, is that when journalism gets involved it should try to poke holes in it instead of reporting the findings as if they were the word of God.
Check Here but I doubt it will help you very much. There are several unsovled conjectures. The Fibonacci Sequence does have application to epidemiology because it describes the way things reproduce.
Garde la Foi, mes amis! Nous nous sommes les sauveurs de la République!
(Keep the Faith, my friends! We are the saviors of the Republic!)
I think the point he is making, is that when journalism gets involved it should try to poke holes in it instead of reporting the findings as if they were the word of God.
It's a matter of expertise. If a journalist with a specific area of scientific expertise is able to evaluate the scientific or technical merit of a research project then let him comment on it. When you understand, however, that scientists from one field are hardly qualified to evaluate the research of another field, you realize the level of expertise required may not be commonly prevalent in journalists.
Anyone can criticize a research project. When a journalist engages in this for the benefit of himself or the lay public, who is to determine whether there is any merit to his criticism?
As an investigative endeavor, science concerns itself with the business of poking holes in research and finding proper explanations for phenomena. The reported findings are the intermediate and end result of that investigation. A distortive journalistic screen is neither helpful nor necessary.
Should journalists be arbiters of what is 'good' or 'bad' science?
Yes, there ought to be lay critics.
In mainstream journalism, such journalists are simply second-hand critics, and usually have ulterior objectives in order to please the fashion of their bosses. This is why in academia the notion of academic freedom is important. There (and with public policy institutes) good lay criticism is naturally available, provided there is academic freedom, to poke holes, .
Criticism by the non-specialist has been popular ever since the time of the 16th century when academia worked hard to loose itself from the stricture of the church. There is political rhetoric in place today which has taken a cue from church politics and it happily works to delegitimize non-specialist criticism. Granted that scientists in one field cannot always follows scientists in another, it seems that the business of poking holes falls especially to the non-specialist's task and that task is not concerned with the particular technological idiosyncracy of the research, but rather quick to raise a stink about a pretentious blunders in the field. Such criticism is possible and necessary because the specialist's myopia is prone to confuse the part for the whole and succumbs to a totalizing vision of reality. Such specialization engages in the abuse of reason, as F.A. Hayek aptly names it, namely, the "the mechanical and uncritical application of habits of thought to fields different from those in which they have been formed." The specialist's study forms conclusions that are valid within its myopic ambit. Very likely, only the specialist understands exactly how those conclusions are valid. The blunder occurs when the validity of a well-argued study is taken as what validates non-specialist conclusions. Non-specialist conclusions which arise out of valid scientific research are blundersome.
You advocate lay criticism and then define criticism by experts in other fields as lay criticism. In some small sense, this could be called lay, however, except for general criticisms between fields, the real criticisms, the hole poking that takes place, is done by those who work in the overlapping edges of fields or in the regions close to it. Major blunders are not rooted out by journalists. They are rooted out by people in the field. How was a journalist going to determine that the Pons-Fleichmann results weren't repeatable? It's left to other experts to criticize myopic blunders.
Blunders of a different type that escape the myopic vision of the experts should not escape the common sense of those around him. Common sense is not restricted to the lay public or the journalist.
You advocate lay criticism and then define criticism by experts in other fields as lay criticism.
Yes, and I would also include the criticism of non-experts in other fields.
except for general criticisms between fields, the real criticisms, the hole poking that takes place, is done by those who work in the overlapping edges of fields or in the regions close to it.
Certainly that can be the most fruitful criticism. Perhaps we should say that advocating lay criticism simply admits that the non-specialist as well as fringe-speialist criticism is not illegitimate. In a democracy (that wonderful Enlightenment product), the danger will be the unjust usurpation of the specialist prerogative by the demands of the lay critic. This was the revolt of the masses which Ortega vociferated against.
It is true that major blunders are generally not rooted out by journalists. The task of the jouralist is different, and if criticism is there it appears second-hand. But it is strange that popular dialogue concerning first amendment freedoms appear not with the specialists, or with fringe specialists, but with journalism.
Perhaps we should say that advocating lay criticism simply admits that the non-specialist as well as fringe-speialist criticism is not illegitimate.
Bring on the lay criticism. The merit of that criticism can be taken at face value. But don't let the journalist be the arbiter of what passes for meritous research or of what aspect of research the public should be aware of.
meritous? try meritorious
52 Posted on 01/31/2002 12:23:27 PST by francisandbeans
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