A:

A representative sample, like any other type of sample, by its very nature leads to a degree of sampling bias, or sampling error. Analyses that rely on data acquired from any sample cannot be as accurate as analyses using data from the entire population, or all the factors or instances, from which the sample was drawn. However, for financial reasons and time constraints, using samples is often necessary, and the use of certain types of samples, among them representative samples, greatly reduces the degree of the sampling bias of a study and allows for a greater degree of confidence in making statistical inferences about the larger population, factors or instances.

Using representative samples is one of the most effective methods for reducing sampling bias. A representative sample accurately depicts or represents the larger population, factors or instances under study according to the characteristics or qualities under examination. For example, if an analysis involves preferences of a consumer population in a certain area according to gender, the male-female ratio of the representative sample is as close as possible to the male-female ratio of the entire consumer population.

Using the representative sampling method alone is not enough to ensure the bias is negligible, especially when making inferences from the results of the sample regarding the larger population. Random sampling from the general population is also important. In random sampling, each member of the larger population has an equal chance of being chosen. Using the example above, if the consumer population is made up of consumers in a specific state, but the sample is chosen from only two counties, then the sample is more likely to be biased because buyers from other counties did not have an equal chance of representation. The size of the group can also be optimally calculated to reduce sampling bias.

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