Probability and Non-Probability Sampling

A variety of sample designs is available for drawing sample from a population. A fundamental question is whether the sample is selected by a probability mechanism or by some other means. A probability sample has the characteristic that each element in the population has a known and non-zero probability of being included in the sample. As a result, selection biases are as possible to be avoided and statistical theory can be employed to derive the properties of the estimators. A probability sample is also designed that statistical inference to population can be based on measures of variability computed from the sample data. In addition, probability sampling allows us to construct a confidence interval within which the true value of the population parameter is expected to lie.


A non-probability sample, on the other hand, is based on a sampling plan that does not have the above feature. It is a non-random and subjective method of sampling where the selection of the population elements comprising the sample largely depends on the personal judgment or the discretion of the sampler. It is arbitrary and is made on the basis of convenience.

A good number of probability sampling designs are in use. Among the most widely used are simple random sampling, systematic sampling, stratified sampling, multi-stage sampling and probability proportional to size sampling. A detailed exposition of these designs is undertaken in the subsequent chapters.

With non-probability sampling, there are several ways to choose cases to include in the sample. Often we allow the choice of subjects to be made by field workers on the spot. When this thee case, there is greater opportunity for bias to enter the sample selection procedure and to distort the finding of the study. The obvious disadvantages of non-probability sampling are that, since it is not based on the probability mechanism, the investigator cannot claim that his or her sample is representative of the large population. This greatly limits the investigators ability to generalize the findings beyond the specific sample studied. Further, no confidence interval estimation is possible for non-probability sampling.