Wisconsin Governor Scott Walker and his cohort of Republicans might have gone too far in redrawing their State’s political boundaries to their advantage. Last November, a federal district court declared these maneuvers, called gerrymandering,* unconstitutional. However, as discussed in this Chicago Tribune article, the Supreme Court might consider overturning the ruling—these gerrymanders being partisan, not racially discriminatory.

One of the most infamous of all gerrymandered districts—1992’s 12th Congressional District in North Carolina-is pictured here. It became known as the “I-85 district” due to being no wider than the freeway for stretches that connected the desired populations of voters.

North Carolina’s 12th was a kind of in vitro offspring of an unromantic union: Father was the 1980s/1990s judicial and administrative decisions under the Voting Rights Act, and Mother was the partisan and personal politics that have traditionally been at redistricting’s core. The laboratory that made this birth possible was the computer technology that became available for the 1990s redistricting cycle. The progeny won no Beautiful Baby contests.

— North Carolina Redistricting Cases:  the 1990s, posted at Minnesota Legislature Web Site

You may wonder, as I did, how gerrymandering works. The latest issue of Nature explains it with their graphic on “packing and cracking” here. Also, see the figures on measuring compactness. Mathematicians approach this in various ways, e.g., the area of the district compared to with that of the smallest polygon that surrounds it (called the convex hull). Quantifying the fairness of boundaries creates a great deal of contention–which measure to use being chosen for greatest advantage of whomever is wielding the figures.

Partisan gerrymandering, if not outlawed, will be catalyzed by the 2020 census. Keep an eye on this.

*A word coined in 1812 when Massachusetts’s Governor Gerry redrew a district north of Boston into the shape of a salamander.

CBS News this morning reported the prediction by New York Times statistician Nate Silver on who will be our next President.  OK, now that you know (presuming you could not resist following the link), how sure are you that it’s accurate?  After all Silver is the author of The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t—published only a month or so ago.  My hunch is that Silver does as well as anyone—given so many unknowns that cannot be known, not the least of which is the fickle nature of undecided voters who might en masse switch allegiance the day of the election.  Anyways, I am viewing his prediction the same as a weather forecast two days out, that is, with a good deal of skepticism but, nevertheless, appreciation for the science behind the modeling.*

PS.  A friend asked me this week whether averaging polls is really valid.  I suppose so based on Silver doing it.  See how he does it at this detailing by him in his “538” blog (538 is the number of electors in the United States Electoral College).

*For example, within 72 hours of a hurricane’s landfall, meteorologists now predict the bulls-eye within a 100-mile radius—compared to 350 miles 25 years ago.  They did really well forecasting Sandy as reported here by The Washington Post.

That was the view of 2nd-year PhD student Douglas VanDerwerken up until this Presidential election.  He abstained on the basis of the lack of return on investment for spending the time to vote when it really cannot make a difference.  VanDerwerken lays it all out for statistics magazine Significance in an article for their current (October) issue.*  According to his reckoning, there is less than one chance in a million (4.5×10^-7 to be precise) of any person’s vote having an impact.  This would be a situation where the voter lives in a swing State and the election comes to a dead heat.

Fortunately (in my opinion—being one who views it as a civic duty) VanDerwerken had an epiphany based on moral reasons, so he shall vote.  Thank goodness!

“If you think about it, voting in a large national election – such as the US Presidential election – is a supremely irrational act, because the probability that your vote will make a difference in the outcome is infinitesimally small.”

– Satoshi Kanazawa, rational choice theorist**

* “The next President: Will your vote decide it”

**See Kanazawa’s three-part series on “Why Do People Vote” for his blog “The Scientific Fundamentalist” hosted by Psychology Today. Start with Part 1 posted here and continue on to the end for the answer.

“Unbelievable jobs numbers..these Chicago guys will do anything..can’t debate so change numbers.”

– Jack Welch

Thursday morning I attended a briefing on the economy by an expert from Wells-Fargo bank.  Looking over the trends in USA unemployment rates he noted that no incumbent since World War II has achieved re-election when joblessness exceeded 8 percent.  Friday the Bureau of Labor Statistics (BLS) announced that the the national unemployment rate is now 7.8%, an improvement from 8.1% last month.    How accurate is this number and is it precise enough that a 0.3% difference can be considered significant?  I agree with the conclusion of this critique posted by Brookings Institution  that “a large part of monthly unemployment fluctuations are spurious.”  So, really, all this fuss about it being 8.1 versus 7.8 percent is really silly from a statistical point of view.  However, it is entertaining!

“Businesses conduct hundreds of thousands of randomized trials each year. Pharmaceutical companies conduct thousands more. But government? Hardly any.”

–David Brooks, The New York Times, 4/26/12 editorial seen here

For those of us in the know about statistical tools this statement provides light at the end of a long tunnel.  However, this columnist gets a bit carried away by the idea that an FDA-like agency inject controlled experiments throughout government.

Although it’s great to see such enthusiasm for proactive studies based on sound statistical principles, I prefer the lower-profile approaches documented by Boston Globe Op-Ed writer Gareth Cook in this May 2011 column.  He cites a number of examples where rigorous experiments solved social problems, albeit by baby steps.  Included in his cases are “aggressively particular” successes by a group of MIT economists who are known as the “randomistas”—a play on their application of randomized controlled trials.

Evidently the obvious success of Google (12,000 randomized experiments in 2009, according to Brooks) and others reaching out over the internet has caught the attention of the mass media.  Provided they don’t promote randomistas running wild, some good will come of this, I feel sure.