[smu3l]

A couple of decades ago I was taking a class at UNC on survey sampling methods. Topics included designing sampling schemes that were efficient in the statistical and actual cost sense, developing variance estimators based on your sampling scheme, etc.

For example if you want to observe and measure some attribute of classes at public schools in your county, it might be infeasible to send data collectors to all of 15 schools, but the marginal cost of measuring additional classrooms at the same school once you're there is minimal. So, how many schools should you visit and how many class rooms per school given a budget and assumptions on inter and intra school variation?

We had had a group assignment to estimate the average circumference of trees on campus. Our initial plan something like 1) get a map of campus and split it into zones 2) sample zones randomly 3) everyone goes to a few (small) zones and tries to roughly map out the trees there 4) sample again from those trees and physically measure them. This would mean running around campus for at least a few days if we wanted to an honest job. And it was a rainy spring in North Carolina.

However, one of my group mates had a stroke of brilliance and decided to email the grounds department. To our surprise they were able to provide us with a full list of every known tree on campus as well as GIS data with locations. So we were able to do a legitimate simple random sample which was optimally efficient from in terms of both variance and time-in-rain.

In conclusion I'm pro list-of-individual-trees.