Archive for August, 2016

“Bright line” rules are simple but not very bright

Just the other day a new term came to light for me—a “bright line” rule.  Evidently this is commonplace legal jargon that traces back to at least 1946 according to this language log.  It refers to “a clear, simple, and objective standard which can be applied to judge a situation” by this USLegal.com definition.

I came across the term in this statement* on p-values from American Statistical Association (ASA) on statistical significance:

“Practices that reduce data analysis or scientific inference to mechanical ‘bright-line’ rules (such as ‘p < 0.05’) for justifying scientific claims or conclusions can lead to erroneous beliefs and poor decision-making.”

The ASA goes on to say:

“Researchers should bring many contextual factors into play to derive scientific inferences, including the design of the study, the quality of the measurements, the external evidence for the phenomenon under study, and the validity off assumptions that underlie the data analysis.”

It is hard to argue that if the p-value is high, the null will fly, that is, results cannot be deemed statistically significant.  However, I’ve never bought into 0.05 being the bright-line rule.  It is good to see ASA dulling down this overly simplistic statistical standard.

I can see the value for “bright line rules” in legal processes, a case in point being the requirement for the Miranda warning being given to advise US citizens of their rights when being arrested.  However, it is ludicrous to apply such dogmatism to statistics.

*(The American Statistician, v70, #2, May 2016, p131)

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Models responsible for whacky weather

Watching Brazilian supermodel Gisele Bundchen sashay across the Olympic stadium in Rio reminded me that, while these fashion plates are really dishy to view, they can be very dippy when it comes to forecasting.  Every time one of our local weather gurus says that their models are disagreeing, I wonder why they would ask someone like Gisele.  What does she and her like know about meteorology?

There really is a connection of fashion and statistical models—the random walk.  However, this movement would be more like that of a drunken man than a fashionably-calculated stroll down the catwalk.  For example, see this video by an MIT professor showing 7 willy-nilly paths from a single point.

Anyways, I am wandering all over the place with this blog.  Mainly I wanted to draw your attention to the Monte Carlo method for forecasting.  I used this for my MBA thesis in 1980, burning up many minutes of very expensive main-frame computer time in the late ‘70s.  What got me going on this whole Monte Carlo meander is this article from yesterday’s Wall Street Journal.  Check out how the European models did better than the Americans on predicting the path of Hurricane Sandy.  Evidently the Euros are on to something as detailed in this Scientific American report at the end of last year’s hurricane season.

I have a random thought for improving the American models—ask Cindy Crawford.  She graduated as valedictorian of her high school in Illinois and earned a scholarship for chemical engineering at Northwestern University.  Cindy has all the talents to create a convergence of fashion and statistical models.  That would be really sweet.

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