The Assistant Village Idiot is doing a series on fashionable politics. 

I had a rather entertaining post about doomsday prep all set for today, but then I looked at the calendar.

Six years ago today, at a trivia night, I got introduced to a guy who perfectly complemented my trivia strengths and weaknesses.  I knew I must get this delightful person (who could always remember who was in what movie, or what musician did what song and when) to be on my trivia team for as long as possible.  When he beat me at Trivial Pursuit, I knew I had to marry him.

The chances of love at first sight are small, and the chances of finding someone who would put up with someone who does literature searches and statistical breakdowns of optimal household and relationship management are even smaller, so I feel pretty darn lucky to have him in my life.

While the genders are reversed, I like the way this video puts it (linked to in case the embedding doesn’t work).
http://videosift.com/widget.js?video=226713&width=540&comments=15&minimized=1
Put more simply (from Andrew Gelman‘s whole post on the topic):
You are perfect; I’d make no substitutions
You remind me of my favorite distributions
With a shape and a scale that I find reliable
You’re as comforting as a two parameter Weibull
When I ask you a question and hope you answer truly
You speak as clearly as a draw from a Bernoulli
Your love of adventure is most influential
Just like the constant hazard of an exponential.
With so many moments, all full of fun,
You always integrate perfectly to one.


Love you honey!

It occurred to me recently that it’s a bit odd that most of my “real world” exposure to research comes in the form of the variety of clinical trials that go on around me on a regular basis, and yet I rarely comment on clinical trials.

This is probably because most clinical trials are a little dense to get through, and the results tend to be less interesting to people (it turns out the reuptake limitations actually weren’t as dramatic as they made them out to be!) and there’s rarely much media involvement to mix things up.

Anyway, I heard a tidbit recently about when to be suspicious of results of clinical trials that I thought I’d pass along.

In any trial assessing a new treatment/drug/etc vs a placebo, you would expect to see more dropouts in the “treated” arm of the study.  This, of course, is because most drugs/treatments have very real side effects that will bother people and cause them to drop out.  Therefore, if you see a trial where the dropout rate is higher in the placebo arm, you should be suspicious.  Placebo studies should almost always be blinded for the patients (and ideally for the providers), but if significantly more of those in the placebo arm drop out, you know this has gone wrong.  Patients don’t keep showing up if they know they’re not actually getting treated with anything…and once we’ve established that the patients know which arm of the study they’re in, the results become much less reliable.

I thought that was an interesting tidbit to keep in mind.

Do you like Johnny Cash?  Do you like data visualizations? Ever wondered how far he travels in “I’ve been everywhere man?”

The answer is 181075 kilometers.

Thank you internet.

FYI, I’m done with 75% of my Christmas shopping.  Still have to get a tree though.

For those of you not done yet, I’ll help you out with what to give me.   Here’s a whole list!  

And for my little genius baby, I’m thinking this “Outlier” bodysuit would be perfect….or perhaps a stuffed normal distribution?

Alright, enough shopping.  Need some entertainment?  Try the “thanks textbooks” tumblr. Featuring the best of the worst problems/examples/etc in textbooks.  Highlights in the commentary include “I’m less concerned with the question, “What does the scale read?”  and more concerned with the question, “Why the hell are we lubricating a hamster?”  and “Who has a “favorite” orange?  How long have you had this orange that you’ve bonded with it so much?  Who has an equation to calculate the weight of an orange?Is it your favorite because it happens to weigh nine pounds!?”

I wanted to put up a brain teaser yesterday, but the little one got his first cold.  Baby coughs are sad.

Anyway, one of the more famous statistical brain teasers is the birthday problem.  There are a few variations, but essentially the question goes something like this:

You’re at a party with 23 guests, including you.  What are the chances that  two people there have the same birthday?

The trick of course is that no one has to have a specific birth date, so the answer is not 23/366, but instead around 50% (interestingly, if the party were 50 people, it goes up to 97%).    For a further explanation, see here.

What’s interesting about this problem is that you have to assume every birth date is equally likely…which of course isn’t true.  I’ve written before about uneven distribution of birthdays in the US, due in part to scheduled c-sections or induced labor.  Anyway, I saw an interesting heat map today of birthday distributions from the Daily Viz, which is what got me thinking about the brain teaser.

 To note, this chart was made from a list of ranked birthdates, which is here.

I was a little struck by this, because I was thinking about how terrible I am at estimating things like this on my own.  The most common birthday in my circle of friends/family is Halloween.  The first week in April has the birth dates of my mother, sister and husband.  Neither of those time frames are overly popular within the general population, although I’d guess the difference between “most popular” and “least popular” are relatively small.  It was a good reminder that those I spend the most time with are not terribly representative of the population in general, on average.

Ann Althouse linked to a local news story about a hospital in Minnesota that went 62 hours and 19 deliveries without delivering a baby girl*.

The AVI has a poll up on polling, in reference to my post about polls.

I’ve recently been considering going more in depth with my stats education (especially the data analytics software stuff), and am checking out a few grad programs in applied statistics.

Anyone have any good suggestions?

Online and/or located in New England preferred.