In the context of public opinion polling, the terms population, sample, and sampling error are closely related.
1. Population: The population refers to the entire group of individuals or objects that the researcher is interested in studying or making inferences about. It represents the larger target group that the poll aims to capture. For example, if the poll is about the approval ratings of a political candidate, the population would be all eligible voters in a particular area.
2. Sample: A sample is a smaller subset of the population that is selected for the purpose of conducting a survey or study. It is not feasible or practical to survey the entire population, so a sample is taken as a representative subset. The sample should ideally mirror the characteristics of the population in order to provide accurate and reliable results. For example, if the population consists of 1000 eligible voters, a sample of 200 voters may be selected for the poll.
3. Sampling error: Sampling error refers to the discrepancy or variation between the results obtained from the sample and the true characteristics or opinions of the entire population. It is a measure of the uncertainty or potential bias introduced by sampling. Sampling error can arise due to various factors, such as random chance, non-response bias, or sampling methods that do not accurately represent the population. The magnitude of the sampling error is often quantified as a margin of error, which indicates the range within which the true population parameter is likely to fall.
In summary, the population represents the entire group of interest, the sample is a smaller subset chosen from the population for surveying, and sampling error is the measure of uncertainty or discrepancy between the sample results and the characteristics of the entire population. Accurate sampling methods and reducing sampling error are crucial in public opinion polling to ensure that the findings from the sample are representative and applicable to the larger population.