Statistical Random Sampling
Statistical Random Sampling is a method pollsters often use to evaluate the public’s perception of an issue or candidate.
Hundreds of millions of voters are formally “polled” on Election Day; however, literally polling every single American citizen before election day is virtually impossible. So, pollsters select a sample of individuals representing the whole population. Samples are collected using two distinct strategies: Random Digit Dialing (RDD) and Self-Selected Samples (SSS). RDD includes randomly calling voters for surveys. RDD can become more effective when pollsters only call registered voters (registration-based RDD). SSS is most commonly a random sample selected from eligible individuals who have signed up to be members of a panel. SSS is considered “non-probability sampling” because when someone opts into a poll, they are seeking to voice their opinion. SSS is often criticized as less reliable since it is not a truly random sample.
A sample chosen randomly is meant to be an unbiased representation of the total population. If the sample does not represent the population, the variation is called a sampling error.
