The three concepts that have been chosen for this analysis are: an estimate, an estimator, and sampling distribution. The term estimate refers the value whose quantity is unknown, and it is often based on data that have been generated from observation. It can also be defined as a certain value of an estimator, which has been gathered from specific data sample and applied to show a parameter value. For example, suppose a retail manager needed to know his customers’ expenditure mean over the last month, he could calculate the population mean by working out the average expenditure of the thousands, but instead of doing this he would use the estimate of the mean of the population. This is found by computing the representative customers’ sample mean. If the calculated value was established to be $300, then $300 would represent the retailer’s estimate.
Another concept to be explored is that of an estimator, which can be defined as the calculated value of quantity from particular set of data that is used to provide information on unknown quantity of the population sample. For instance, the mean of a sample is taken as the estimator of its population mean. An estimator can be applied in the insurance company’s business to provide information on the potential insured clients. The final term used in this analysis is a sampling distribution, which can be defined as the probability distribution of a statistic that is generated as a result of drawing a random sample from a population. Moreover, sampling distribution can be used to calculate TOEFL scores for the United States high school pupils in a particular year.