I always dread the inevitable "What do you do?" question. When you tell someone you are in market research you can typically expect a blank stare or a polite nod; so you must be prepared to offer further explanation. Oh, to be a doctor, lawyer or auto mechanic – no explanation necessary!
Of course, as researchers, we grapple with this issue daily, but it is not often we get to hear it played out on major news networks. After one of the debates, I heard Wolf Blitzer on CNN arguing (yes arguing) with one of the campaign strategists about why the online polls being quoted were not "real" scientific polls. Wolf's point was that because the Internet polls being referenced were from a self-selected sample their results were not representative of the population in question (likely voters). Of course, Wolf was correct, and it made me smile to hear this debated on national TV.
A week or so later I heard an even more, in-depth consideration of the same issue. The story was about how the race was breaking down in key swing states. The poll representative went through the results for key states one-by-one. When she discussed Nevada she raised a red flag as to interpreting the poll (which has one candidate ahead by 2 - % points). She further explained it is difficult to obtain a representative sample in Nevada due to a number of factors (odd work hours, transient population, large Spanish speaking population). Her point was that they try to mitigate these issues, but any results must be viewed with a caveat.
Aside from my personal delight that my day-to-day market research concerns are newsworthy, what is the take-away here? For me, it reinforces how important it is to do everything in our power to ensure that for each study our sample is representative. The advent of online data collection, the proliferation of cell phone use and do-it-yourself survey tools may have made the task more difficult, but no less important. When doing sophisticated conjoint, segmentation or max-diff studies, we need to keep in mind that they are only as good as the sample that feeds them.