Carl Beijer’s argument about sources of support for Trump is an excellent example of how variance-based thinking beats means-centric thinking.
For a means-based thinker, Trump’s message is about trade, racism, and other ideas that don’t make a great deal of sense. However, since his message can be placed on a map, so to speak, it follows this line of thinking that his followers are motivated by the mean of his message that can be calculated. Since the mean of his message lies in the realm of insanity, it follows that those who follow his message must be as crazy as well.
The variance-centric thinking does not presume to measure the mean of Trump’s message: it is all over the place so that calculating the mean does not make much sense. (If the distribution is very fat, you can be very far away from the mean and still catch a meaty part of the distribution.) They key is simply that Trump is different, drawing an usual, but very identifiable following who may or may not take his proposals seriously. The important thing is to identify those who are–and why they don’t fit the existing paradigm, rather than why they fit Trump’s paradigm. To paraphrase Tolstoy, if all unhappy families are different, asking why they are unhappy will yield too many answers none of which will be universally true. It makes more sense to ask why aren’t they happy–that is, what their deviations are from the formula of the happy families and start the analysis from there. (In the like vein, the taste preference of Bud Light drinkers cannot be cleanly identified–they are a heterogeneous group who choose Bud Light either because it is cheap, they like the ads, they have no taste, they had odd taste that is not being met by anyone else, or because they genuinely like Bud Light. If you think you can profit by identifying the “Bud Light taste” and profit by jacking up the price, you will be in for a huge disappointment.)
So the Trump voters are…different and heterogeneous, but they are similar in that they are unhappy with the status quo and are drawn to Trump for many different but understandable reasons–most of which are directly or indirectly tied to the economic and social changes. Trying to put them all in the box, by identifying their mean and using it as a stand in for them all is bound to disappoint because their variance is so huge.
PS. In a sense, it is really a matter of knowing the limits of your data: can you profit by knowing the true mean of Trumpists’ (or Bud Light drinkers’) preferences? What is the variance on their tastes, preferences, or motivations? If they come with sufficiently small variance, then yes, you do have a lot to gain by knowing their mean preference with precision. If they don’t, then all the extra data will only give you an erroneous belief that you know what the truth is, not the truth itself, which is inherently too variable to be pinned down.