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There appears to be a new blog at Science Blogs, and it is APPLIED STATISTICS!!!!! That's the name of the blog, and that's what it is about, and the blogger is Andrew Gelman. Welcome Andrew. We needed a stats blog, and it is very nice to have you here.
So now, a statistics joke:
Three statisticians go rabbit hunting. They wait patiently in the hunting blind for several hours and finally a rabbit comes hopping into view, and pauses to munch on the carefully laid out bait.
By pre-arranged lottery, Statistician 0 fires his rifle at the rabbit, but the bullet shoots right over the rabbit's head, missing the target.
Statistician 1 then takes aim and fires. But the bullet's trajectory is low, and it penetrates the ground in front of the rabbit, who is now rather bewildered at this strange turn of events.
Statistician 2 lifts his rifle, and places it backwards over his shoulder, stands up and starts to head back to the car.
"Well, we got him!" he intones, as he heads home, happy to have had another average day.
Go visit Applied Statistics
Comments
A statistician would momentarily grin but be highly offended by this joke. Need I remind you of all the statistical fallacies that you violated with this joke???
Okay, it was funny. :-D And it's nice to have somewhere in addition to 538.com to go to for my statistics fix.
Posted by: The Science Pundit | October 31, 2009 10:21 PM
In the end, the wabbit had the last laugh.
Posted by: Greg Laden | October 31, 2009 10:24 PM
Old joke, but stats blog??!! Woo Hoo!
Going there now & hoping to find it share-worthy...
Posted by: badrescher | October 31, 2009 10:41 PM
So, like, is there a link for this new blog? Science Blogs doesn't seem to know about it by that name...
Posted by: clheiny | November 1, 2009 12:37 AM
There does seem to be a link in the post in the last sentence.
Posted by: Irene | November 1, 2009 2:17 AM
Doh! I was trying to use the "Go To: Choose a blog" menu. Thanks for the pointer.
Posted by: clheiny | November 1, 2009 2:37 AM
oh, gosh, that's sweet, especially after reading this 'WTF' piece on Science (yes, THAT Science):
http://sciencenow.sciencemag.org/cgi/content/full/2009/1030/1
Sheesh. How can any competent researcher not summarize p-value in 15 words or less??
Posted by: Michael Spencer | November 1, 2009 6:16 AM
I think that a very large number of researchers think p-value is something other than it is, and among quasi experts you have people splitting hairs that drive any reason out of the conversation. During the exchange you link to, the journalist provided a perfectly adequate explanation that works for any member of the public and it was rejected and complexified by the scientist. I think the scientist was misinterpreting what the journalist was saying and assuming that he was conflating p-values with 'success' rates of a certain treatment.
Say you want to know what a compression test does on a car, because you are thinking about buying a car. It is perfectly OK to know that a compression test below a certain value means the engine has a high probablity of needing to be expensively repaired or replaced soon, and in the mean time you might not have much power and you might be burning oil. That is not, of course, what a compression test really is, but to exlain the details and have the car buyer understand them is too hard, and the car buyer does not really need to or want to know all this shit, and is not likely to end up really learning anything useful.
A simple statement that "sometimes statistics can show a successful outcome by accident, or random chance. The lower the p-value, the less likely that the particular study is wrong in this way." is perfectly adequate. More than adequate. And more than close to reality than "the car will be blowing oil and you'll have to shell out big bucks within months of buying it"
Statistics is not free of a cultural wrapping, and most stuff coming out of the front-hole by people who supposedly know what they are talking about is no better than what comes out of the backhole, either because they don't really know, or because it is not really in their interest to be plainly understood by non-experts.
That applies generally to expertise, but perhaps to statistics more than a lot of other things. That whole conversation (at Science) is a great example of it being hard to distinguish front-hole from back-hole effluent.
Posted by: Greg Laden | November 1, 2009 11:39 AM
I read his blog and saw that last week.
http://www.stat.columbia.edu/~cook/movabletype/mlm/
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Posted by: Btok | December 12, 2009 4:12 PM