Careers Business Ownership How Probability and Nonprobability Samples Differ Share PINTEREST Email Print Andy Roberts/Getty Images Business Ownership Operations & Success Market Research Sustainable Businesses Supply Chain Management Operations & Technology Marketing Business Law & Taxes Business Insurance Business Finance Accounting Industries Becoming an Owner By Gigi DeVault Gigi DeVault LinkedIn Twitter University of Washington San Jose State University University of California, San Diego Gigi DeVault is a former writer for The Balance Small Business and an experienced market researcher in client satisfaction and business proposals. Learn about our Editorial Process Updated on 07/22/19 A sample is a subset, or smaller group, within a population. When designing studies, researchers must ensure that the sample replicates the larger population in all the characteristic ways that could be important to the study's research findings. Some samples so closely represent the larger population that it's easy to make inferences about the larger population from your observations of the sample group. In market research, there are two general approaches to sampling: probability sampling and nonprobability sampling. Generally, nonprobability sampling is a bit rough, with a biased and subjective process. This sampling is used to generate a hypothesis. Conversely, probability sampling is more precise, objective and unbiased, which makes it a good fit for testing a hypothesis. Probability Sampling In the technique of probability sampling, also known as random sampling, everyone in the population has an equal chance of being chosen as a representative sample: Everyone in the sample must have the same probability, or fixed opportunity, to be in the sample set.andThe probability of any member of the sample group being selected for the sample can be mathematically calculated. In other words, everyone has the same, a fair chance of being selected. The characteristics of probability sampling can be summarized as follows: Random basis of selection Fixed, known opportunity of selection Used for conclusive research Produces an unbiased result The method is objective Can make statistical inferences The hypothesis is tested Nonprobability Sampling One of the most noteworthy features of the method of nonprobability sampling, also known as nonrandom sampling, is that there isn't any specific probability that any given person will be in the sample set. In other words, you don't know which person from a population will be chosen for the sample. Some characteristics of nonprobability sampling include: Arbitrary basis of selectionUsed for exploratory researchProduces a biased resultUses a subjective methodCan make analytical inferencesThe hypothesis is generated An Important Limitation of Nonprobability Sampling With nonprobability sampling, inferences cannot be drawn about the larger population based on a nonprobability sample. This is not always the case, however, since a realistic view of how people approach research findings readily identifies situations where people do inappropriately draw conclusions from findings associated with nonprobability samples. Potential Sampling Errors When working with nonprobability 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 occurs a result of nonparticipation, which can have an important effect on the overall outcome of a study. For example, in the 1980 General Society Survey (GSS), 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. Convenience Sampling 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, although 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, although even careful matching may not result in samples free of bias. The possibility of bias from hidden sources always exists. Purposive Sampling 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 (that is, according to the "normal curve") in the larger population. Purposive sampling is fraught with bias, some of which occurs as a result of the methods 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 TBI 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. 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, for example. These samples must be highly representative of the population in order to make reliable forecasts.