Carolyn Browne Tamler

I’ve talked in a previous Blog about polling, but in light of the fact that we are seeing new polls everyday, and they show widely different results, I feel compelled to, once again, review the concept of a random sample survey. In theory, that’s what a poll is supposed to be. A random sample means that everyone in a given population has an equal chance of being selected, and a mathematical principle determines the margin of error. Anything that alters this concept biases the results.

Here are some things that will impact the accuracy of the responses and allow the results to be improperly projected to an entire population:

If you sample everyone who is registered to vote—this will include a lot of people who will not vote;
If you sample likely voters in a way that excludes certain elements of the population (such as neighborhoods with a lot of low-income or minority populations, or that will exclude many people who may be likely to vote, but were not included in the sample;
If you sample using a self-select method (such as a Survey Monkey or a robo-call survey), it will exclude people who simply have no interest in responding.

With the wide spectrum of responses from the many different polls being conducted daily, it is obvious that they are using different sampling methodologies and/or asking questions in ways that bias the responses.

I have given up looking at any one poll for any sense of what is going on and I have taken to following Nate Silver’s Blog in the New York Times. Others who I respect say that he has been the most accurate reader of the polls and what they mean. Here’s the link to the Blog: