In: Statistics and Probability
In the given example, the 100 children selected from each of the 50 states is the sample. The population is all children in the United States. A population is the group in which the researcher is interested in and a sample is a subset of individuals selected from the population to study the characteristics of the population.
Mean of the the sampling distribution will be 8 years, according to central limit theorem. The population mean and mean of sampling distribution is same according to central limit theorem.
Probability sampling techniques are the techniques in which the probability of an individuals to be selected as sample can be calculated. Simple random sampling and systematic sampling are two probability sampling techniques. In non probability sampling, we cannot estimate the probability of an individual getting selected for sample. Convenience sampling and snowball sapling are two non probability sampling techniques.
Probability sampling is recommended over non probability sampling because, the former can be representative of the population while the we can't say the same about the later. We cannot say how much representative a sample is, if the sample taken using non probability sampling.
Sampling is important because we cannot conduct study on populations that are large. It will be time consuming, expensive and at times, practically impossible. So, we need to chose an appropriate sampling techniques and get a sample that will express the traits of the population.