The "Wing-Nut" Principle

Anyone who has ever taken mathematics in general or statistics in particular should be familiar with the graph of "normal distribution"

This graphs the distribution of values of a variable or characteristic that has a "continuous, randomly distributed variable or characteristic" spread over a population.

The points that many people miss are that the characteristic must be "continuous" and the distribution must be "random". Continuous means that the characteristic must be capable of existing at any (even fractional) percentage of the population between zero and some maximum (the saturation amount). A non-continuous distribution would result in multiple bumps - one for each favored percentage. An example would be the distribution of people along a bus route - where they congregate at bus stops.

Random means that there is no pattern to what the value of the characteristic takes on in any instance. The graph simply shows the mean (value at which the number of points greater is equal to the number of points less) as the center value. The tails on either side never really stop if the population is large enough - there is always the possibility that the amount of the characteristic can be infinitely high to one degree or low to the other.

Many studies have been done of various aspects of society and the world, and in many of these the results resemble the standard distribution curve; sometimes skewed in one direction or the other a bit, but mostly looking pretty much "standard".

One of the aspects of today's highly connected society is that the subjects of some of these studies become actively involved in evangelizing the topic of the study. In many cases, it is the people on the extreme ends of the graph who decide that they don't like being one of the few - but instead want to be one of the many. They actively recruit people a little closer to the norm - moving them farther from the mean and closer to the fringe. These people in turn work at recruiting people closer to the mean than they are - and so it goes.

This now violates the requirement of randomness in the distribution - so the graph of the distribution of the particular characteristic no longer looks like the standard deviation curve. The participants are actively changing their own and other's measured characteristic.

As an example, measuring the population on whether they were active hunters of wild animals or active protectors of wild animals as little as 30 to 50 years ago would have shown a fairly typical standard curve.

In more recent times, the activities of those out on the fringes at either end have moved people from the middle towards one or the other fringes - flattening out the curve.

But what happens when the the population becomes "polarized" - with few people being "wishy-washy" or ambivalent towards the fringes - and ending up choosing to be one or the other extreme? We then end up with a graph that looks more like: 

Sure looks like a "wing-nut" to me... - maybe that's the source of the euphemism typically used to designate someone pretty far out on one or the other end of the scale :)