Archive for August, 2012

Random thoughts

The latest issue of Wired magazine provides a great heads-up on random numbers by Jonathan Keats.  Scrambling the order of runs is a key to good design of experiments (DOE)—this counteracts the influence of lurking variables, such as changing ambient conditions.

Designing an experiment is like gambling with the devil: only a random strategy can defeat all his betting systems.

— R.A. Fisher

Along those lines, I watched with interest when weather forecasts put Tampa at the bulls-eye of the projected track for Hurricane Isaac.  My perverse thought was this might the best place to be, at least early on when the cone of uncertainty is widest.

In any case, one does best by expecting the unexpected.  That gets me back to the topic of randomization, which turns out to be surprisingly hard to do considering the natural capriciousness of weather and life in general.  When I first got going on DOE, I pulled numbered slips of paper out of my hard hat.  Then a statistician suggested I go to a phone book and cull numbers from the last 4 digits from whatever page opened up haphazardly.  Later I graduated to a table of random numbers (an oxymoron?).  Nowadays I let my DOE software lay out the run order.

Check out how Conjuring Truly Random Numbers Just Got Easier, including the background by Keats on pioneering work in this field by British (1927) and American (1947) statisticians.  Now the Australians have leap-frogged (kangarooed?) everyone, evidently, with a method that produces 5.7 billion “truly random” (how do they know?) values per second.  Rad mon!


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It’s the letter of the law: Stand down with Calibri

Twenty years ago or so I cajoled the advertising rep from R&D Magazine into lending me a binder filled with several inches of ‘white papers’ of the publisher’s research on readership.  Their data came primarily from A/B (split) testing—not very sophisticated but effective for simple comparisons.  One question I resolved was whether to use serif or sans serif font.  The research showed significant advantages to headlines being san serif, such as Arial font, and text in serif—for example, Times New Roman.  I’ve stuck with that ever since,* except for the fonts themselves changing over to Calibri and Cambria—the defaults in current versions of Microsoft Office software.

However, now I am set back by this news from Wall Street Journal that Calibri comes up short—30 percent to be precise—versus Arial and other common fonts, at least so far as the State of Michigan is concerned.  The inventor of Calibri, Lucas de Groot, justifies his type being smaller because of its high readability per square inch.  Although this seems plausible to me, I would like to see the research supporting this assertion.

For an interesting detailing of fonts—serif versus san serif and neo-grotesque versus humanist—see this blog by Laurie Israel Think.

*For writings that will likely be read in printed form, that is.  Having seen research like this recent study from the JOURNAL OF COGNITIVE PSYCHOLOGY, I believe that words written in a sans serif font provide a significant advantage for messages read on computer screens, such as blogs and email.  Thus for these purposes I prefer using Calibri exclusively—ditto for presentations projected on screen, for example—using Powerpoint.

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Statisticians no more—now “data scientists”

I spent a week earlier this month at the Joint Statistical Meetings (JSM)—an annual convocation of “data scientists”, as some of these number crunchers now deem themselves.  But most statisticians remain ‘old school’ as evidenced by this quote:

“Some time during the past couple of years, statistics became data sciences older, more boring sibling that always played by the rules.”

— Nathan Yau*

I tend to agree—being suspicious of changes in titles as a cover for shenanigans.  It seems to me that “data science” provides a smoke screen to take unwarranted leaps from shaky numbers.  As the shirt sold at JSM by American Statistical Association (ASA) says, “friends don’t let friends extrapolate.”

*Incorrectly attributed initially (my mistake) to Carnegie Mellon statistics professor Cosma Shalizi, who was credited by Yau for speaking up on this subject.