I'll keep it simple, so everybody can follow along.
In the data set used by the CRU, there are two types of data covering modern times, real data, and proxy data. Real data comes from thermometers, while proxy data is estimated based on indicators like tree ring growth.
Obviously, when we're talking about temperatures from the 15th and 16th centuries, we're using proxy data, estimating temperatures based on tree growth. Well a funny thing happens when you do that. According to the tree rings, the Medieval Climate Optimum, a time when the earth was significantly warmer than today, didn't occur. Ice core samples still show the MCO, as do anecdotal accounts, and archaeological records that show settlements in Greenland which are now covered by a permanent glacier. But according to the CRU, since it isn't in the tree rings, it didn't happen.
But wait, there's more!
According to those same tree rings, the hockey stick doesn't show up either.
Bummer.
So those crazy kids at the CRU decide to ignore the proxy data for modern times and just graft on the real measured data. This is fine except that they don't go back and apply a correction to the Medieval data!
This is called cooking the books. If the tree ring data requires a correction in modern times to track with actual temperatures, then the correction should be applied throughout the series, including during the MCO. Think of it in terms of the 2000 Presidential election. AL Gore wanted a recount, but only in counties that favored him. The SCOTUS rightly decided that this would result in an inaccurate count, and told Florida to count it all, or don't count. We have a similar situation here. The CRU is using the data that works best for their hypothesis.
An honest application of the correction factor would show warming in the 20th century, but less than that during the MCO. In other words, while the earth is warming, we are not outside the range or rate of normal climatological change.
But there's no money or power in that conclusion, is there?
Saturday, December 12, 2009
Using ‘real data’ to get a false result
Monday, December 07, 2009
Global Warming: As Valid as the Piltdown Man
Every time I look into climate change dogma, I find more deliberate deception. Take this story for example, which details exactly how researchers "cherry-picked" the data to produce the famous Hockey Stick that Al Gore uses to beat nay sayers into submission. It's a hoax, as artificial as the Piltdown Man, and ironically, just as widely accepted by scientists.
It took 40 years for paleontologists to realize that Piltdown Man was a fraud. We don't have 40 years this time. Our politicians are busy using the hammer of global warming to destroy our freedom. Using Global Warming as an excuse, the EPA is ready to start regulating the discharge of greenhouse gases like CO2 and methane as pollutants. In case you forgot grade school biology, you discharge CO2 every time you breath, and methane with every fart.
Yes, this means that a Federal Agency will soon be capable of regulating every breath you take, not to mention how often you can have that burrito for lunch.
Far fetched? It wasn't too long ago that OSHA tried to regulate safety standards in your private home if you had a home office.
And that is the driver behind all of this. It's less about protecting the Earth than it is about controlling our behavior. Let's be honest for a moment. If these climate dictator wannabees truly believed there was a risk, would they be behaving as they do? Flying in private jets to Copenhagen? Traveling in limousines? Living high on the hog like AL Gore? When you show me politicians taking real action to reduce their carbon usage (and buying carbon offset credits from your own company doesn't cut it, Al.) then I'll believe they are serious. When the scientists (for lack of a better word) decide to operate transparently, sharing their data publicly instead of hiding it for 10 years, answering critics with facts instead of attacks; when they start revising hypotheses to fit the observed facts instead of the other way around, then I'll take them seriously. When they can show me a computer model that accounts for all of the data, not just the subsets that agree with their hypothesis, and that makes predictions with a margin of error smaller than the predicted deviation, and a track record of accuracy better than the local weatherman, well, then I'll take it seriously.
Until then, global warming theory is more sideshow than science, and I've never been a big fan of clowns.
Except for Red Skelton.
(H/T Volokh Conspiracy)
It took 40 years for paleontologists to realize that Piltdown Man was a fraud. We don't have 40 years this time. Our politicians are busy using the hammer of global warming to destroy our freedom. Using Global Warming as an excuse, the EPA is ready to start regulating the discharge of greenhouse gases like CO2 and methane as pollutants. In case you forgot grade school biology, you discharge CO2 every time you breath, and methane with every fart.
Yes, this means that a Federal Agency will soon be capable of regulating every breath you take, not to mention how often you can have that burrito for lunch.
Far fetched? It wasn't too long ago that OSHA tried to regulate safety standards in your private home if you had a home office.
And that is the driver behind all of this. It's less about protecting the Earth than it is about controlling our behavior. Let's be honest for a moment. If these climate dictator wannabees truly believed there was a risk, would they be behaving as they do? Flying in private jets to Copenhagen? Traveling in limousines? Living high on the hog like AL Gore? When you show me politicians taking real action to reduce their carbon usage (and buying carbon offset credits from your own company doesn't cut it, Al.) then I'll believe they are serious. When the scientists (for lack of a better word) decide to operate transparently, sharing their data publicly instead of hiding it for 10 years, answering critics with facts instead of attacks; when they start revising hypotheses to fit the observed facts instead of the other way around, then I'll take them seriously. When they can show me a computer model that accounts for all of the data, not just the subsets that agree with their hypothesis, and that makes predictions with a margin of error smaller than the predicted deviation, and a track record of accuracy better than the local weatherman, well, then I'll take it seriously.
Until then, global warming theory is more sideshow than science, and I've never been a big fan of clowns.
Except for Red Skelton.
(H/T Volokh Conspiracy)
Thursday, December 03, 2009
Last Word on ClimateGate
Does the fact that leading climate scientists had to fudge the data automatically prove that Anthropogenic Global Warming does not exist?
No.
But what it does prove is that the politics have overwhelmed the science. An honest look at the data reveals that we are not facing an imminent crisis, and even more importantly reveals that we don't know enough about how the global climate works to be able to devise an intelligent response to the non existent crisis. We certainly don't know enough to subject ourselves to the absurd policies being pushed by Al Gore and his merry band of con men. Policies that they themselves fail to adhere to, which should tell you something about their level of integrity.
The simple fact is that the climate is far too complex for us to model with any accuracy, as is born out by the failure of every computer model to predict the cooling that's taken place over the past decade. It is also a fact that impact of the annual increase in atmospheric greenhouse gases is far too small to estimate with any accuracy.
Should we continue to study the issue? Absolutely, but let's do it openly and honestly, without all of the hype and fear mongering. Should we look for ways to continue to reduce our impact on the planet? Of course! My mom taught me to clean up after myself as a child; I see no reason for stopping now. I fully support smart, alternative energy sources, like nuclear, as well as distributed power generation, like home solar (PV and thermal) and wind. I'm all for becoming energy self sufficient, and for reducing the use of fossil fuels as practical.
But we don't need to institute draconian federal or global policies that will result in a greatly decreased standard of living both here and elsewhere in response to some looming crisis that doesn't exist.
No.
But what it does prove is that the politics have overwhelmed the science. An honest look at the data reveals that we are not facing an imminent crisis, and even more importantly reveals that we don't know enough about how the global climate works to be able to devise an intelligent response to the non existent crisis. We certainly don't know enough to subject ourselves to the absurd policies being pushed by Al Gore and his merry band of con men. Policies that they themselves fail to adhere to, which should tell you something about their level of integrity.
The simple fact is that the climate is far too complex for us to model with any accuracy, as is born out by the failure of every computer model to predict the cooling that's taken place over the past decade. It is also a fact that impact of the annual increase in atmospheric greenhouse gases is far too small to estimate with any accuracy.
Should we continue to study the issue? Absolutely, but let's do it openly and honestly, without all of the hype and fear mongering. Should we look for ways to continue to reduce our impact on the planet? Of course! My mom taught me to clean up after myself as a child; I see no reason for stopping now. I fully support smart, alternative energy sources, like nuclear, as well as distributed power generation, like home solar (PV and thermal) and wind. I'm all for becoming energy self sufficient, and for reducing the use of fossil fuels as practical.
But we don't need to institute draconian federal or global policies that will result in a greatly decreased standard of living both here and elsewhere in response to some looming crisis that doesn't exist.
Monday, November 30, 2009
Signal to Noise Ratio and Data Collection
We're going to try an experiment. You'll need two items, a TV and a radio, or two of either. Tune one to a good station and the other to static. Now, turn both volumes all the way down. If you're using TVs, we don't care about the picture, just the sound.
Alright. The first thing we're going to do is to turn up the volume on the good station. Turn it up loud enough so you can hear it clearly.
Got it?
Okay, now, turn up the static until you can just barely hear it.
Can you still understand the first station? If not, turn it up just a bit higher.
This is a demonstration of "Signal to Noise." The good station is the signal, and the static is the noise. As the signal volume is significantly greater than the noise volume, you can still understand what is going on.
Now let's turn up the static until it is just as loud as the signal.
Can you still understand what is coming over the good channel?
You can probably catch bits and pieces, maybe even enough to get the general idea, but you're going to miss a lot. The signal and the noise are at the same volume, or amplitude, and some of the signal is getting lost in the noise.
Now turn up the static until it is twice as loud as the clear station.
Now the signal is completely lost in the noise. In order to get the signal, we'll have to either amplify it or filter out the noise. Either way, we'll corrupt a small portion of the data, but we'll recover far more than we lose.
In essence, this is what we mean when we talk about the signal to noise ratio. The higher the ration, the easier it is to access and understand the signal. This same principle is at work when we are collecting data on a complex system. When there is more than one variable involved, and we're only wanting to study one of them, then we have a lower signal to noise ratio, as all the changes due to the other variables are noise, and we have to filter them out. Just like in our example, no matter what filter we use, we're going to lose some accuracy in our data. The amount of accuracy we lose depends on the ratio. The lower the ratio, the greater the loss of accuracy.
Applying this idea to climatology brings a very important fact to light. The signal is far smaller than the noise, by several hundred times. As an example, annual temperature fluctuations in Tennessee span 80F or more. Daily temperatures can vary by 30F. Yet the annual change the global warming folks are looking for, the signal, is a fraction of a degree. The signal is lost inside an overwhelming amount of noise. The only way to filter that much noise out is to make assumptions about the signal, and this is where climatologists got into trouble. They made assumptions about the behavior of the climate, and used those assumptions to massage the data to get the result they were looking for. This is not an attempt at fraud, simply an attempt to pull a usable signal out of an extremely noisy dataset. The problem is that there is no way to validate the filter assumptions in a timely manner. They plugged the assumptions into climate prediction models, then used the same assumptions to process the newly gathered data.
In essence, they created a circular formula where the data was processed to reinforce the assumptions that were used to process the data. Programmers have a term for this:
Garbage in; Garbage out.
Alright. The first thing we're going to do is to turn up the volume on the good station. Turn it up loud enough so you can hear it clearly.
Got it?
Okay, now, turn up the static until you can just barely hear it.
Can you still understand the first station? If not, turn it up just a bit higher.
This is a demonstration of "Signal to Noise." The good station is the signal, and the static is the noise. As the signal volume is significantly greater than the noise volume, you can still understand what is going on.
Now let's turn up the static until it is just as loud as the signal.
Can you still understand what is coming over the good channel?
You can probably catch bits and pieces, maybe even enough to get the general idea, but you're going to miss a lot. The signal and the noise are at the same volume, or amplitude, and some of the signal is getting lost in the noise.
Now turn up the static until it is twice as loud as the clear station.
Now the signal is completely lost in the noise. In order to get the signal, we'll have to either amplify it or filter out the noise. Either way, we'll corrupt a small portion of the data, but we'll recover far more than we lose.
In essence, this is what we mean when we talk about the signal to noise ratio. The higher the ration, the easier it is to access and understand the signal. This same principle is at work when we are collecting data on a complex system. When there is more than one variable involved, and we're only wanting to study one of them, then we have a lower signal to noise ratio, as all the changes due to the other variables are noise, and we have to filter them out. Just like in our example, no matter what filter we use, we're going to lose some accuracy in our data. The amount of accuracy we lose depends on the ratio. The lower the ratio, the greater the loss of accuracy.
Applying this idea to climatology brings a very important fact to light. The signal is far smaller than the noise, by several hundred times. As an example, annual temperature fluctuations in Tennessee span 80F or more. Daily temperatures can vary by 30F. Yet the annual change the global warming folks are looking for, the signal, is a fraction of a degree. The signal is lost inside an overwhelming amount of noise. The only way to filter that much noise out is to make assumptions about the signal, and this is where climatologists got into trouble. They made assumptions about the behavior of the climate, and used those assumptions to massage the data to get the result they were looking for. This is not an attempt at fraud, simply an attempt to pull a usable signal out of an extremely noisy dataset. The problem is that there is no way to validate the filter assumptions in a timely manner. They plugged the assumptions into climate prediction models, then used the same assumptions to process the newly gathered data.
In essence, they created a circular formula where the data was processed to reinforce the assumptions that were used to process the data. Programmers have a term for this:
Garbage in; Garbage out.
Religion, Not Science: How to Tell the Difference
Some of us understood from the beginning that anthropogenic global warming was a myth. The thermodynamics didn't work. The predictive models failed time and time again. The overwrought scenarios of Al Gore and others failed to materialize. Overhyped claims of 'proof' inevitably fell short of proving anything.
Still, the myth persisted.
I would like to believe that most of the scientists involved were acting in good faith (and we'll come back to that word in just a moment) and not engaged in deliberate fraud, but it is hard to hold to that belief when I read emails detailing ways to suppress any research that criticizes global warming orthodoxy. When the methods of that suppression involve the deliberate destruction of reputations, it becomes even more difficult.
Global Warming has been used to predict a higher than average number of hurricanes and to explain a lower than average number of hurricanes. It has been used to explain floods, droughts, the flourishing of species, and the extinction of species. It has been used to predict the expansion of deserts and an accelerated ice age. Global warming is blamed for heat waves and hard freezes, as well as extended moderate weather patterns. In short, every weather related phenomena has been attributed to Global Warming.
Raw climate data is hidden from public scrutiny. In at least one case, the raw data has been thrown away, discarded in favor of data modified by the group. The modification methods are kept secret. Computer models are kept secret. Methodologies are guarded jealously.
We are told that the science is too complicated, too difficult for anyone but a select few to understand. This select group is determined not by qualifications or any objective criteria, but by how well the hold to the orthodoxy. The group works to define the orthodoxy, and to defend it against any detractors.
We know that several groups routinely acted to massage the data to make it fit the theory, instead of the other way around. Computer code was written to produce the desired result, instead of the one that most accurately represented the data.
I could go on, but you get the point. Well, some of you will anyway. The true believers in the Church of Global Warming will not lose the faith, no matter what is said.
Still, the myth persisted.
I would like to believe that most of the scientists involved were acting in good faith (and we'll come back to that word in just a moment) and not engaged in deliberate fraud, but it is hard to hold to that belief when I read emails detailing ways to suppress any research that criticizes global warming orthodoxy. When the methods of that suppression involve the deliberate destruction of reputations, it becomes even more difficult.
Sign 1. Critics are treated as heretics.
Global Warming has been used to predict a higher than average number of hurricanes and to explain a lower than average number of hurricanes. It has been used to explain floods, droughts, the flourishing of species, and the extinction of species. It has been used to predict the expansion of deserts and an accelerated ice age. Global warming is blamed for heat waves and hard freezes, as well as extended moderate weather patterns. In short, every weather related phenomena has been attributed to Global Warming.
Sign 2. Every question has the same answer.
Raw climate data is hidden from public scrutiny. In at least one case, the raw data has been thrown away, discarded in favor of data modified by the group. The modification methods are kept secret. Computer models are kept secret. Methodologies are guarded jealously.
Sign 3. Belief comes from faith, not a knowledge of the facts.
We are told that the science is too complicated, too difficult for anyone but a select few to understand. This select group is determined not by qualifications or any objective criteria, but by how well the hold to the orthodoxy. The group works to define the orthodoxy, and to defend it against any detractors.
Sign 4. An elite group is formed, priests, to spread the faith and punish the unbelievers.
We know that several groups routinely acted to massage the data to make it fit the theory, instead of the other way around. Computer code was written to produce the desired result, instead of the one that most accurately represented the data.
Sign 5. When fact and belief collide, belief wins.
I could go on, but you get the point. Well, some of you will anyway. The true believers in the Church of Global Warming will not lose the faith, no matter what is said.
Tuesday, November 24, 2009
What Does the Definition of Species have to do with Global Warming?
It used to be straightforward. Instead of relying on what the animal looked like, or it's phenotype, they relied on what it was based on DNA, or it's genotype. The rule was fairly simple. If two animals could breed and produce viable offspring, that is, their babies could make more babies, then they were the same species. If they produced sterile offspring, they were different species, but the same genus. If they could not produce offspring, then they were part of a different family. This clear cut system was abandoned by Ernst Mayr because it caused an insurmountable problem with evolutionary theory, e.g. speciation could not be demonstrated as occurring in multi-cellular organisms like animals. He added 'reproductive isolation' to the mix. This has nothing to do with DNA and everything to do with location. If a group of animals of one species is isolated from the rest of the species, whether through geography or some other factor, that group becomes a separate species. Even though it is genetically compatible with the rest of it's members, the isolated group will, according to theory, begin to diverge from the original and will eventually become reproductively distinct.
Using this definition, wolves and dogs were considered to be separate species, despite the fact that they interbreed in nature. Many bird species are capable of interbreeding, and will do so in nature and in captivity. As an example, check out web resources on breeding conures, where there is a great deal of concern about losing species to hybridization.
What is clear is that reproductive isolation is a concept that is less about science than it is about philosophy. It gives evolutionary theory a tremendous boost, but causes tremendous scientific problems otherwise.
As another example, check out this story about how global warming is causing grizzlies and polar bears interbreeding
Grizzlies and polar bears mate in the wild when they are in contact during mating season. They've always fit the classical definition of species, and now they fit Mayr's as well.
So, why should we care about whether biologists calls these animals one species or two?
Well, it points out something that many on the left would like us to forget: scientists are people too. They have their own prejudices, biases, and yes, agendas. The data they gather is viewed through the lens of their expectations, and once they've built a model for a process, they are extremely reluctant to revise it, even in the face of overwhelming data. Definitions are changed to make observed data fit a predetermined model, rather than changing the model to fit the data.
It's not a conspiracy, or a plot; it's usually just human nature.
Usually.
We've all heard the stories of global warming activists exaggerating to 'raise awareness." Al Gore won an Oscar for that. We've also heard about "mistakes" like NASA's Goddard Institute for Space Studies using September climate data to claim that October 2008 was the hottest October ever recorded. That incident was particularly interesting as the GISS initially denied the error, making up reasons for the findings before coming clean about the error.
While these incidents, and the dozens like them are embarrassing for the Climate Change crowd, things just got a whole lot worse.
Hackers recently released hundreds of emails and data files, including computer models used to determine climate trends. There are multiple instances in these files of scientists openly planning to alter data to fit the desired outcome. There are comment remarks in computer code that are even more blunt, saying that data will be adjusted to match "the real temperature."
Most of us assume that the data would be the real temperature, and any manipulation would introduce error, but we don't think like scientists.
Again, I'm not claiming a global scientific conspiracy here, just normal, human tunnel vision. These men and women have invested thousands of man hours and billions of dollars of other people's money into climate change research. The models they built represented the sum of all that effort, time, and expense. When the data didn't fit, it's a very human failing to want to explain away the data instead of going to the guy who pays your check and say, "Well, we were wrong." So they naturally look for ways to throw out the data that doesn't fit. They invent plausible sounding reasons for doing so, and they publish their "enhanced" results. Other groups working in the field are now under pressure to corroborate the findings, and adopt the same "enhancements."
It's human nature, and each and every one of us has experienced exactly the same reaction, and done the same thing. Remember, scientists are not priests; they are not unbiased or impartial, no matter what the stereotype is.
The difference is that there are unscrupulous politicians applying very real pressure to these research institutes to come up with the "right" answers, right being defined as the answer which gives the government the most control.
The bottom line is that science is not the clean, neat, theoretical process we have pictured in our minds. Scientists are not dispassionate robots, acting solely on logical analysis of the data. Science is messy, confusing, and contradictory, and scientists are just as subject to pettiness and corruption as any other human being.
Using this definition, wolves and dogs were considered to be separate species, despite the fact that they interbreed in nature. Many bird species are capable of interbreeding, and will do so in nature and in captivity. As an example, check out web resources on breeding conures, where there is a great deal of concern about losing species to hybridization.
What is clear is that reproductive isolation is a concept that is less about science than it is about philosophy. It gives evolutionary theory a tremendous boost, but causes tremendous scientific problems otherwise.
As another example, check out this story about how global warming is causing grizzlies and polar bears interbreeding
Polar bears face a new threat besides melting ice — male grizzly bears are moving into their territories, competing for food and are even mating with their females.
Scientists have already discovered one case of a hybrid “grolar” bear and are circulating requests to hunters and polar tour operators to look out for more...
Scientists suggest that the white coat of polar bears evolved because paler creatures would have had an advantage in hunting seals.
In genetic terms, however, such differences are superficial. In captivity polar bears and grizzlies can interbreed, with their offspring also being fertile — a sign that their DNA is similar. :
Grizzlies and polar bears mate in the wild when they are in contact during mating season. They've always fit the classical definition of species, and now they fit Mayr's as well.
So, why should we care about whether biologists calls these animals one species or two?
Well, it points out something that many on the left would like us to forget: scientists are people too. They have their own prejudices, biases, and yes, agendas. The data they gather is viewed through the lens of their expectations, and once they've built a model for a process, they are extremely reluctant to revise it, even in the face of overwhelming data. Definitions are changed to make observed data fit a predetermined model, rather than changing the model to fit the data.
It's not a conspiracy, or a plot; it's usually just human nature.
Usually.
We've all heard the stories of global warming activists exaggerating to 'raise awareness." Al Gore won an Oscar for that. We've also heard about "mistakes" like NASA's Goddard Institute for Space Studies using September climate data to claim that October 2008 was the hottest October ever recorded. That incident was particularly interesting as the GISS initially denied the error, making up reasons for the findings before coming clean about the error.
While these incidents, and the dozens like them are embarrassing for the Climate Change crowd, things just got a whole lot worse.
Hackers recently released hundreds of emails and data files, including computer models used to determine climate trends. There are multiple instances in these files of scientists openly planning to alter data to fit the desired outcome. There are comment remarks in computer code that are even more blunt, saying that data will be adjusted to match "the real temperature."
Most of us assume that the data would be the real temperature, and any manipulation would introduce error, but we don't think like scientists.
Again, I'm not claiming a global scientific conspiracy here, just normal, human tunnel vision. These men and women have invested thousands of man hours and billions of dollars of other people's money into climate change research. The models they built represented the sum of all that effort, time, and expense. When the data didn't fit, it's a very human failing to want to explain away the data instead of going to the guy who pays your check and say, "Well, we were wrong." So they naturally look for ways to throw out the data that doesn't fit. They invent plausible sounding reasons for doing so, and they publish their "enhanced" results. Other groups working in the field are now under pressure to corroborate the findings, and adopt the same "enhancements."
It's human nature, and each and every one of us has experienced exactly the same reaction, and done the same thing. Remember, scientists are not priests; they are not unbiased or impartial, no matter what the stereotype is.
The difference is that there are unscrupulous politicians applying very real pressure to these research institutes to come up with the "right" answers, right being defined as the answer which gives the government the most control.
The bottom line is that science is not the clean, neat, theoretical process we have pictured in our minds. Scientists are not dispassionate robots, acting solely on logical analysis of the data. Science is messy, confusing, and contradictory, and scientists are just as subject to pettiness and corruption as any other human being.
Sunday, October 25, 2009
No, You Don’t Have the Swine Flu
This is really getting old.
First of all, if your doctor tells you that you have the H1N1 2009 variant, aka Swine Flu, less than 24-48 hours after your doctor visit, he's just guessing. It takestime to culture the virus and then identify it, and if he hasn't done that, then all he really knows is that you have a respiratory infection of some type.
Don't believe me? Okay, here's CBS saying the same thing.
Some numbers from the article:
California tested 13,704 blood samples from people with flu like symptoms. These folks were given a provisional diagnosis of swine flu. When the lab tests came back, only 2% came back positive for the H1N1 variant. That's 274 cases out of 13,704 diagnoses. 86% of the samples, and remember, these were all from people who were told they probably had the swine flu, had no flu viruses at all.
What does this mean to you and me? First of all that the hype and panic about swine flu is about as real as global warming. It also means that there are a lot of people out there who believe that they are immune to the swine flu since they've been told they already had it, and those people may be in for one heck of a surprise should the real thing hit. And finally, it means that President Obama's declaration of a national emergency is based on faulty numbers generated by hysteria.
First of all, if your doctor tells you that you have the H1N1 2009 variant, aka Swine Flu, less than 24-48 hours after your doctor visit, he's just guessing. It takestime to culture the virus and then identify it, and if he hasn't done that, then all he really knows is that you have a respiratory infection of some type.
Don't believe me? Okay, here's CBS saying the same thing.
If you've been diagnosed "probable" or "presumed" 2009 H1N1 or "swine flu" in recent months, you may be surprised to know this: odds are you didn’t have H1N1 flu.
In fact, you probably didn’t have flu at all.
Some numbers from the article:
California tested 13,704 blood samples from people with flu like symptoms. These folks were given a provisional diagnosis of swine flu. When the lab tests came back, only 2% came back positive for the H1N1 variant. That's 274 cases out of 13,704 diagnoses. 86% of the samples, and remember, these were all from people who were told they probably had the swine flu, had no flu viruses at all.
What does this mean to you and me? First of all that the hype and panic about swine flu is about as real as global warming. It also means that there are a lot of people out there who believe that they are immune to the swine flu since they've been told they already had it, and those people may be in for one heck of a surprise should the real thing hit. And finally, it means that President Obama's declaration of a national emergency is based on faulty numbers generated by hysteria.
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