To give a little preview of the content of the (very short) paper:<p>> In statistics, the standard error has a clear technical definition: it is the estimated standard devi- ation of a parameter estimate. In practice, though, challenges arise when we go beyond the simple balls-in-urn model to consider generalizations beyond the population from which the data were sampled. This is important because generalization is nearly always the goal of quantitative studies. In this brief paper we consider three examples:<p>> What is the standard error when the bias is unknown and changing (my bathroom scale)?<p>>How do you interpret standard errors from a regression fit to the entire population (all 50 states)?<p>> How should we account for nonsampling error when reporting uncertainties (election polls)?
Is it true that the standard deviation is the "expected" distance from the mean of a randomly selected value?<p>Like, if the mean is 1 and the SD is 1, and I was to take a sample and take the average of the absolute values of the samples, would that tend towards 1 as I take more samples?<p>Edit: I think I've just described "mean absolute error"... seems to be related but the "root mean square deviation" is the one more closely related to SD. I think... Not sure where the "n" comes in.