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A sample is called a simple random sample if each element of the population has the same chance of being included in the sample.

There are several techniques of selecting a random sample, but the concept that each element of the population has the same chance of being included in a sample forms the basis of all random sampling, namely simple random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. These four different types of sampling schemes are usually referred to as sample designs.

Since collecting each data point costs time and money, it is important that in taking a sample, some balance be kept between the sample size and resources available. Too small a sample may not provide much useful information, but too large a sample may result in a waste of resources. Thus, it is very important that in any sampling procedure, an appropriate sampling design is selected. In this section, we will review, very briefly, the four sample designs mentioned previously.

Before taking any sample, we need to divide the target population into nonoverlapping units, usually known as sampling units. It is important to recognize that the sampling units in a given population may not always be the same. Sampling units are in fact determined by the sample design chosen. For example, in sampling voters in a metropolitan area, the sampling units might be individual voters, all voters in a family, all voters living in a town block, or all voters in a town. Similarly, in sampling parts from a manufacturing plant, the sampling units might be an individual part or a box containing several parts.