Representative Sample

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What is a 'Representative Sample'

A representative sample is a small quantity of something that accurately reflects the larger entity. An example is when a small number of people accurately reflect the members of an entire population. In a classroom of 30 students, in which half the students are male and half are female, a representative sample might include six students: three males and three females.

BREAKING DOWN 'Representative Sample'

When a sample is not representative, the result is known as a sampling error. Using the classroom example again, a sample that includes six students, all of whom are male, would not be a representative sample.

A representative sample parallels the key variables and characteristics under examination. Some examples include sex, age, education level, socioeconomic status or marital status. Using a larger sample size increases the likelihood that the sample more accurately reflects what actually exists in the population. Any information collection with biased tendencies is unable to generate a representative sample.

Reasons to Use a Representative Sample

A representative sample allows the collected results to be generalized to a larger population. For most marketing or psychology studies, it is impractical in terms of time, finances and effort to collect data on every person in the target population. This is especially impractical for large population such as an entire country or race.

Risks of Using Samples

The use of sample groups poses risks, as the sample may not accurately reflect the views of the general population. One of the largest risks is developing a sample that is not truly representative. This most likely occurs because the population group is too small. For example, when comparing data relating to gender, a representative sample must include individuals of different ages, economic status and geographical locations. Such information typically requires a diversification of information-collecting sites.

Random Sampling and Purposive Sampling

Random sampling involves choosing respondents from the target population at random, to minimize bias in a representative sample. While this method is more expensive and requires more upfront information, the information yielded is typically of higher quality. Purposive sampling is more widely used, and occurs when the managers target individuals matching certain criteria for information extraction. Ideal interview candidates receive profiles. Although this leads to the potential of bias in the representative sample, the information is easier to collect, and the sampler has more control when creating the representative sample.

True Representative Samples Cannot Exist

When developing a survey, the manager must utilize controls to track and monitor who has provided input, whether the information is usable, and whether it can be interpreted. Random sampling ensures every member of the population has equal probability of selection and inclusion in the sample group. However, sample bias is always present and can never truly be eliminated. For example, individuals who are too busy to participate will be under-represented in the representative sample, as they are less likely to provide feedback.

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