Inferring and Explaining

96 InferrIng and exPlaInIng fIgure 5. Median weekly earnings, 2004–2014 Retrieved from Bureau of Labor Statistics, The Economics Daily , https://www.bls.gov/opub/ted/2014/ ted_20141028.htm. All this stuf—“the seasonally adjusted civil- ian unemployment rate,” “infation adjusted median weekly earnings,” and the like—really matters for a number of reasons. Te biggest concern, of course, is that most of my readers have bills to pay, families to support, and fnan- cial plans to make for their futures; what their paycheck is, and what it buys them, are of para- mount importance. In addition, politicians of all stripes demand their votes because the economy is doing so well or because it is doing so poorly. Finally, as good explanation seekers, we would all like to know what’s going on. Were it my paycheck, my vote, or simply my intellectual curiosity, I’d probably take an eco- nomics course or two, read a bit more about where the parties and their candidates stand on all this, and as you may have guessed, apply the methods of inference to the best explanation to all this statistical data. Samples and Populations We will use the term population as jargon for any sort of a group—a group of people; a group of things, such as vehicles that get better than thirty miles per gallon; or groups of very abstract things, such as depictions of Santa Claus in primetime television. We can use the mathematician’s notion of a set to characterize a population. Similarly, we will use the term sam- ple as jargon for any part of the group constitut- ing the population. Tus samples are subsets of the set making up the population. In a familiar Venn diagram, the lighter, smaller oval consti- tutes the sample and the darker, larger oval the population. Very ofen we are interested in samples because we assume that they can tell us something inter- esting about the population. You might well ask, If we are really interested in the population, why we wouldn’t just look at it directly? And the