Learn How Probability and Non-Probability Samples Differ

Fourteen white eggs, one brown egg
Andy Roberts/Getty Images

Samples are an important part of market research since making direct observations of all members of a population that are under study is generally not feasible. A sample is a subset of a population. Care must be taken to ensure that the sample corresponds with the larger population in all the ways that could be important to the research findings of the study. Some samples so closely represent the larger population that it is not problematic to make inferences about the larger population based on observations of the sample group.

Two Approaches: Probability Sampling Versus Non-Probability Sampling

There are two general approaches to sampling in market research: Probability sampling and non-probability sampling. Probability sampling must meet the following conditions: Every unit of analysis must have the same probability of being included in the sample group, and then the mathematical probability of any member of the sample group being selected for the sample can be mathematically calculated.

What Is Sampling Error and How Do I Know If I Have It?

When working with non-probability samples, it is important to understand the occurrence of sampling error. The smaller the sampling group, the greater the chance of sampling error. One particular type of bias is a result of non-participation. It is important to make understand the impact of non-participation on the overall outcome of a study. One example comes from the 1980 General Society Survey (GSS) in which those who did not participate in the research were found to be quite different — as a group—from those who had participated.

The hard-to-reach group members were significantly different from their peer labor force participants—most markedly in socioeconomic status, marital status, age, the number of children, health, and sex.

What Is Convenience Sampling? Is It Convenient to Analyze?

Convenience samples are commonly used in social science and behavioral science because of the heavy reliance on college students, patients, paid volunteers, members of social networks or formal organizations, and even prisoners.

The purpose of much social science and behavioral science research is to verify that certain characteristics occur or do not occur in the group undergoing study. A common approach is to look for relationships among several attributes. Convenience samples are useful and adequate for this type of study. Also, it is useful to recognize that a convenience sample is not always easy to put together.

Convenience samples may also be matched in order to compare two groups. In order to use matched convenience samples, a researcher must be able to identify a counterpart for each member of the first sample. These counterparts are members of the second (matched) sample. The variables that are commonly matched include gender, age, race, ethnicity, educational attainment, place of residence, political orientation, religion, job type, and wages or salary. Matching these variables helps to reduce sources of bias. However, it is important to recognize that even careful matching may not result in samples free of bias—there is always a possibility of bias from hidden sources.

What Is Purposive Sampling? Is It Always Non-Probabilistic?

Purposive sampling is used when the research design calls for a sample of people who exhibit particular attributes.

Generally, these attributes are rare or unusual and are typically not distributed normally (according to a "normal curve") in the larger population. Purposive sampling is fraught with bias, some of which occurs as a result of the methods that are used to identify the members of a purposive sample. For example, if the research purpose requires studying Veterans with traumatic brain injury (TBI), then the sample must consist of ex-members of the military who have sustained a traumatic brain injury, and who identify themselves accordingly and agree to participate in the study. Each of these attributes or conditions contributes a measure of bias to the sample, thereby limiting the level and type of conclusions that result from the study.

An Important Limitation of the Non-Probability Sampling Approach

An important limitation of non-probability sampling is that inferences cannot be drawn about the larger population based on a non-probability sample.

This is not always the case, however, since a realistic view of how people approach research findings will readily identify situations where people do inappropriately draw conclusions from findings associated with non-probability samples.

Also known as: convenience sampling, purposive sampling

Examples:

Samples that act like public opinion polls are disseminated with the idea that they represent how members of a population will vote in a coming election or the like. These samples must be highly representative of the population in order to be used to make forecasts about election results, for instance.