Carolyn Browne Tamler

Now that election season is ramping up, it seems we hear about new polling results every couple of days….and the results appear to be all over the place. Since random sample surveys are based upon math principles, what accounts for the variations?

How a sample is chosen and how a question is asked determine if a survey result will truly be providing projectable statistically valid data.

A random sample means that everyone in a given population has an equal chance of being selected. Anything that alters this concept biases the results. Right now, we’re hearing a lot about polls relating to the November election. Here are some factors that will affect the accuracy (i.e., the likelihood that the sample results will match the total election results):

Sampling everyone who is registered to vote, rather than only those who are likely to vote, will include a lot of people who will not vote.
Sampling likely voters in a way that excludes certain elements of the population (such as neighborhoods with a lot of low-income or minority populations) will bias the sample.
Sampling using a self-select method (such as Survey Monkey) will exclude people who simply have no interest in responding.

As part of the survey methodology, the survey company will want to have a screening question, (assuming they are using a list of registered voters) such as: “Are you planning to vote in the November election?”

And, surveys are a snapshot of the moment. If a major event happens that is widely reported in the news a few days before the polling is done, that will likely affect the results.