Samples are selected in such a way as to avoid presenting a biased view of the population. The sample will be unrepresentative of the population if certain members of the population are excluded from any possible sample. For example, if a researcher is interested in the drug-usage patterns among teenagers, but collects the sample from schools, the sample is biased because it excludes teenagers not in school for a variety of reasons, such as lack of funds to attend or schooled at home. Biases may also occur if some members of the population are more likely or less likely to be included in the sample than other members of the population for a reason other than the sample design. So the sample collected from schools is also biased because students who miss a lot of school days because of a chronic illness will be less likely to be selected than students who attend regularly.

The best way to avoid a biased or unrepresentative sample, and thus to obtain a representative sample of the population, is to select a random sample, also known as a probability sample. A random sample is defined as a sample in which every individual member of the population has a non-zero probability of being selected as part of the sample. In a simple random sample, every individual member of the population has the same probability of being selected as every other individual member. Other types of random samples fall under the category of complex sample design.

A sample that is not random is called a non-random sample or a non-probability sample. Some examples of non-random samples are convenience samples, judgment samples, purposive samples, quota samples, and snowball samples.

1. Based on the definition for a statistical sample at Wikipedia (accessed 28 December 2006) [disclaimer].