Short Term Global Warming (in the wild)
Let me start by saying I’m not an expert on global warming. I’m absolutely sure the earth is getting warmer (think melting ice caps) and very sure that it is caused by humans (green house gases). But who knows. Remember, if someone yells their dissenting opinion loud enough, it becomes fact, right?
Anyway, I’ve read some articles about how global warming has “stopped” in the last ten years. For instance, this article: “Climatologists Baffled by Global Warming Time-Out” states: “At present, however, the warming is taking a break,” confirms meteorologist Mojib Latif of the Leibniz Institute of Marine Sciences in the northern German city of Kiel. Latif, one of Germany’s best-known climatologists, says that the temperature curve has reached a plateau. “There can be no argument about that,” he says. “We have to face that fact.”
It goes on to say: “Even though the temperature standstill probably has no effect on the long-term warming trend, it does raise doubts about the predictive value of climate models, and it is also a political issue. For months, climate change skeptics have been gloating over the findings on their Internet forums. This has prompted many a climatologist to treat the temperature data in public with a sense of shame, thereby damaging their own credibility.”
This sounds like he is claiming that the warming has stopped. I disagree with this. You can have a system that is, on the average increasing over the long term, while still observing very flat or even declining trends when we know the overall system is increasing. That doesn’t mean that the system isn’t increasing, it just means we’ve seen one realization of the random system that hasn’t increased entirely by chance.
Here is a simulation experiment. (All these numbers are made up, but they prove the point):
Consider at year 1 the average temperature is 75 degrees. Call this x[1]. Then at year two we observe a realization from a normal random variable whose mean is 1.005*75 with standard deviation 1. Call this x[2]. x[3], the temperature in the the third year, will than be an observation from a normal random variable with mean 1.005*x[2] and standard deviation 1.
Over the long run this is an increasing sequence, but let’s look at what happens in the relatively short term. I simulated 10,000 of these such chains for 100 “years” each.
After 5 years 20.4% of these sequences we below the starting temperature of 75 degrees. After 10 years, 12.8% were below 75 degrees. Think about that. We have a known increasing sequence and after 10 years, 12.8% of them ended below where they started. Global warming is like this. We can see small periods of decline, in fact we EXPECT to see small periods of decline, within this increasing sequence.
What happens when we look at this sequence after 50 years? 0.006% are below the starting temperature of 75 degrees. After 100 years, 0 are below 75 degrees.
So to say that global warming is “taking a break” based on ten years of evidence seems like bad science to me. And this is certainly not evidence invalidating the long term usefulness of climate change models.
Cheers.
Obesity and Diabetes (in the wild)
Estimated County-Level Prevalence of Diabetes and Obesity — United States, 2007.
Also be sure to check out this very nice figure which shows the United States at the county level by rate of diabetes and obesity. It’s really striking in this figure just how centralized high levels of obesity are in the southeast and Appalachia. It also show the close relationship between high levels of obesity with a large prevalence of diabetes.
There are two other pockets that strike me as interesting: Northeastern Arizona and the border of North and South Dakota. What explains those high levels of obesity and diabetes? The first thing that comes to mind is that those could possibly be areas with a large population of Native Americans. So I Googled “Native American’s and obesity” and I found this study: “The epidemic of obesity in American Indian communities and the need for childhood obesity-prevention programs.” The first few sentences of the abstract are: “American Indians of all ages and both sexes have a high prevalence of obesity. The high prevalence of diabetes mellitus in American Indians shows the adverse effects that obesity has in these communities. Obesity has become a major health problem in American Indians only in the past 1–2 generations and is believed to be associated with the relative abundance of high-fat foods and the rapid change from active to sedentary lifestyles. Intervention studies are urgently needed in American Indian communities to develop and test effective strategies for weight reduction.”
And this study: “Prevalence of Obesity Among US Preschool Children in Different Racial and Ethnic Groups” (Sarah E. Anderson, PhD; Robert C. Whitaker, MD, MPH. Arch Pediatr Adolesc Med. 2009;163(4):344-348.) which claims these results: “Results Obesity prevalence among 4-year-old US children (mean age, 52.3 months) was 18.4% (95% confidence interval [CI], 17.1%-19.8%). Obesity prevalence differed by racial/ethnic group (P < .001): American Indian/Native Alaskan, 31.2% (95% CI, 24.6%-37.8%); Hispanic, 22.0% (95% CI, 19.5%-24.5%); non-Hispanic black, 20.8% (95% CI, 17.8%-23.7%); non-Hispanic white, 15.9% (95% CI, 14.3%-17.5%); and Asian, 12.8% (95% CI, 10.0%-15.6%). All pairwise differences in obesity prevalence between racial/ethnic groups were statistically significant after a Bonferroni adjustment (P < .005) except for those between Hispanic and non-Hispanic black children and between non-Hispanic white and Asian children."
Cheers.
Stock Market (in the wild)
This article is about Andrew Patton from Duke (which was tweeted by NISSSAMSI had this interesting tidbit about the stock market in it:
“‘Unfortunately, stock prices are almost impossible to predict,’ Patton said. ‘But what we can look at are things like risk and correlation.
‘For instance, it is well known that a given bundle of stocks often decline in value together, but those very same stocks rarely increase in value together,’ he said. ‘In other words, there’s something very different going on in a bear market than in a bull market, and my research tries to capture that difference.'”
I’m pretty sure that is really interesting.
Cheers.
US vs the world: Higher Education (in the wild)
My friend emailed me this article this afternoon:Apples and Oranges in higher education.
It’s all about the use and misuse of statistics in comparing the United State higher education system to other countries. Pretty interesting stuff.
Cheers.
Damn it feels good to be a statistician (in the wild)
Here is some salary information about the statistics profession from amstat.org.
Cheers.
Federalist Papers (in the wild)
Prior to the ratification of the United States Constitution, many of the founders wrote a series of papers supporting the ratification of the Constitution. These papers came to be known as the Federalist papers. When they were written, they were published anonymously. Years later several of the authors claimed their work. However, many of these papers went unclaimed. In the seventies, two statisticians, Frederick Mosteller and David Wallace, tackled the problem of identifying the authors of the unclaimed papers. Here is a brief description of what they did on page 130 of the book “The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century” by David Salsburg
Stats are everywhere.
Cheers.
Halloween! (in the wild)
Welcome to the Halloween edition of StatsInTheWild:
Here are some interesting articles:
THE PARANORMAL: THE EVIDENCE AND ITS IMPLICATIONS FOR CONSCIOUSNESS
PAPERS RELATED TO THE 1995 REVIEW OF THE GOVERNMENT STARGATE PROGRAM
AN ASSESSMENT OF THE EVIDENCE FOR PSYCHIC FUNCTIONING
JESSICA UTTS’ HOMEPAGE
Cheers.
Pfizer Colloquium (in the wild)
I met Stephen Fienberg (not to be confused with Steve Feinberg) today at the Pfizer Colloquim.
This morning he met with all of the grad students, and we got to ask him questions. Somehow we got onto the topic of the flu vaccine and it’s safety, which led to him talking about the link between autism and the M/M/R vaccine. He explained that one paper published in Britain found a link between autism and the M/M/R vaccine when controlling FOR NO OTHER FACTORS. Since then, countless other studies have been unable to find this link. However, Thimerosal, a vaccine preservative, has since been removed from the M/M/R vaccine and the reported number of autism cases continues to rise. This seems to be clear evidence that Thiomersal was not causing Autism. Here is an article about that very subject: Autism Cases Still Going Up As Vaccine Mercury Removed.
Later, he gave a talk where he spoke about his career. The vast array of applied work he has done over his career is just amazing. One topic he spoke about was some work he did for the department of energy about the accuracy of polygraphs which led him to write this book. Applications like these where statistics is applied to real problems in the real world is what this blog is about.
John Tukey once said, “The best thing about being a statistician is that you get to play in everyone’s backyard.” Stephen Fienberg added tonight, “and we get to take their toy’s home with us.”
Cheers.
Evolution (in the wild)
One of the things I’ve always liked about statistics is that the methods can be applied to so many interesting areas. John Tukey expressed this sentiment well, saying “The best thing about being a statistician is that you get to play in everyone’s backyard.” Well said, John. Well said.
Today, let’s play a little bit in evolutionary biology’s backyard: Human Evolution: Are Humans Still Evolving?
Cheers.
evolution
Stats in the Wild 