Types of Sampling A sample should have the same characteristics as the population it is representing. Most statisticians use various methods of random sampling in an attempt to achieve this goal. There are several different methods of random sampling. In each form of random sampling, each member of a population initially has an equal chance of being selected for the sample. A simple random sample is a sample selected from a population in a way such that all combinations of members of the population of that size have the same chance of being selected. However, a true simple random sample can sometimes be difficult to obtain. Additionally, researchers may sometimes wish to ensure that some distinguishable characteristic of members in the population is not overrepresented or underrepresented in their sample, as could occur by chance with a simple random sample. Beyond simple random sampling, well-known random sampling methods include stratified sampling, cluster sampling, and systematic sampling To choose a stratified sample, you should divide the population into groups, and then take your sample from a proportionate number from each group