Q. What is Sample Population?
Choosing a sample population is one of the most important factors in gathering statistical data because all of the claims will be based on the sample data, the data obtained from the sample population.
Often, statistical studies are held to try and find a link or correlation between two different things. For example, does a good diet improve intelligence? Does gender effect job potential? Are men naturally more violent than females?
All of these questions are answered using statistics. You will often see headlines which read Study shows that diet improves intelligence or other similar statements.
The purpose of the sample population is to try and make a general claim about an entire population based on the results of a study on a sample population. This means that your sample needs to be as good of a representation of the population as possible.
Statisticians work very carefully to get a sample population that will help them with their study. A sampling plan is the procedure used to select the elements of the sample population. There are two categories of sampling plan:
1. Judgment Samples: Sample elements are selected on the basis of being typical.
2. Probability Samples: Sample elements are drawn on the basis of probability. In other words, each element of the population has a certain probability of being selected as part of the sample.
When claims or inferences are being made based on a sample we require the sample to be a probability sample.
The most common is probability sample is the random sample, in which every element in the population has an equal probability of being chosen.
Notice that there is a difference between random and haphazard. Random means with equal probability whereas haphazard means without pattern. Often random samples are chosen using a random number generator.