Monday, January 18, 2010

Lord of the Hadrons

Three Particles for the Heisenbergs under the sky,
Seven for the von Neumanns in their halls of stone,
Nine for Mortal Men doomed to die,
One for the Schrödinger on his dark throne
In the Land of Dirac where the Strange lie.
One Particle to rule them all, One Particle to find them,
One Particle to bring them all and in the darkness bind them
To give them Mass where the Charms lie.

[Hat Tip to Sir Al Dente -- Thanks Al! (What a Looney!), and of course JRR Tolkien]

PS: Anyone else remember the Superconducting Super Collider?
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Saturday, January 16, 2010

Richard Simmons PE Crudade Victorious!

A year ago I wrote about Richard Simmons PE Crusade , and there is recent news of considerable progress. MetaDeb tells me that the legislation is now included in this years No Child Left Behind package. This news come from a recent Today Show interview with RS, but I haven't been able to confirm this thru any other source in a casual search. I'm hopeful that it is true.

Richard Simmons' appearance on the Today Show also included this surprising appearance together with William Shatner:

I say that is surprising, because with simmons and Shattner together, I did not know it was possible to fit that much HAM into a single television studio. ;-)
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Wednesday, January 13, 2010

The smell of asparagus

I like it when science and humor cross-pollinate, but sometimes it's even funnier when it's completely serious. You just can't make up stuff like this:

Br Med J  1980;281:1676-1678 (20 December),

A polymorphism of the ability to smell urinary metabolites of asparagus.

M Lison, S H Blondheim, R N Melmed
The urinary excretion of (an) odorous substance(s) after eating asparagus is not an inborn error of metabolism as has been supposed. The detection of the odour constitutes a specific smell hypersensitivity. Those who could smell the odour in their own urine could all smell it in the urine of anyone who had eaten asparagus, whether or not that person was able to smell it himself. Thresholds for detecting the odour appeared to be bimodal in distribution, with 10% of 307 subjects tested able to smell it at high dilutions, suggesting a genetically determined specific hypersensitivity.

Discoblog has a longer exposition on the topic:

NCBI ROFL: Asparagus, urine, farts, and Benjamin Franklin (Part I)

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This and That

Some interesting things I found, with no particular theme:

String Theory Illustrated
[found at Not Even Wrong]

Andrew Gelman comments on Climate change as a religion, and the more general use of "religion" as a term of insult.

Can US Skiing be saved?  As an avid skier, even tough transplanted to the lowlands, it would be fair to say I don't give a damn if the ice caps melt and cities are inundated, just so long as global warming DOESN'T MESS WITH MY SKIING!! [Climate Progress]

A really creepy commenter over at Evolutionblog.

Pallative Care Grand Rounds, with links to the interesting and wonderful, ...

... including PostSecret. Very cool.

Destination Ordination, via the Sensuous Curmudgeon.

Good News for bone marrow transplant patients. [ScienceNow]
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Saturday, January 9, 2010

Hula-Hoops, Confidence Intervals, and Invisible Men

In addition to doing statistical analysis, a big part of my job is explaining the meaning of the results. I am privileged to work with a lot of really smart people on all manner of biomedical and translational research. However, "really smart" does not always mean that everyone is highly numerate; some people just are not good at expressing themselves that way. That's OK too, not everyone can be expected to be good at everything, but it does present a special challenge sometimes when I need to explain something statistical to someone who has difficulty understanding. Somewhat surprisingly, the toughest questions are not about the most complex mathematics. Instead, the hardest questions to answer are often on the most basic concepts.

Case in point, a few days ago I was asked to define a confidence interval for a client who needed to be able it explain the concept to yet another colleague who was asking her. Here I have someone not-too-numerate needing to explain it to someone else likely not-too-numerate, and it's important, so I needed to give a clear and simple definition for her to understand and pass on.

A bit of background before I give my definition; this was relating to an observational study on clinical data, and we have a large number of means, standard errors, and confidence intervals to report. All intervals presented at at a 95% confidence level.

A confidence interval (CI) is a range of values that is likely to contain the actual value we want to know. For the purposes of this paper we have made a lot of single value estimates, or point estimates, of the means and slopes in which we are interested. Although this is not a random sample, we hope that this patient sample is an unbiased representation of a larger group of similar patients. If we were to repeat the study with another group, we would hope to get similar results, as opposed to very different findings. Confidence intervals represent a reasonable range of values that might be the true value that represents the entire population, and not just the single sample we happen to have. The confidence level is the probability, here 95%, that the true value we are trying to determine is actually within the interval.

To make sure I got the full meaning across, I added a bit about how to interpret confidence intervals.

Confidence intervals are very useful for interpreting the clinical importance of a finding. If the low and low ends of the interval are both clinically meaningful and not too far apart, then we can be fairly certain of a sound result even though we have some uncertainty due to sampling error. If the CI is “wide” there might be a lot of room for interpretation of what the result really means. The width of a confidence interval is directly related to the statistical significance. There is also a matter of “clinical significance” – not everything that is statistically significant is clinically meaningful, and vice-versa.

I wrote that up and fired it off in an email, but I felt like I still hadn't gotten it simple enough. I needed an easy non-technical example, and a moment of inspiration hit me; the Hula-Hoop as an analogy to a confidence interval, and an invisible man as the population the interval is trying to capture:

Invisible Man Photo

A simpler/sillier definition: You are throwing a hula-hoop at an invisible man. You can be pretty sure that you have actually caught the invisible man inside the hoop (95%), but you can’t be certain. If it is a big hula-hoop, you still don’t know exactly where he is. If the hula-hoop is small enough then you know almost exactly where he is, or close enough that you don’t care.

Now think about throwing “statistical hula-hoops” at the values you want to know about the general population to report in your study. You have captured most of them inside the hoops (95%), but you still don’t know precisely where they are.

Perhaps not the finest hour of statistical science, but if it gets the point across I'll still be happy with it.

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