Introduction
A sample is a set of data drawn from the population, it is considered as a subset. Populations are defined as the set of all items or individuals of interest. Samples are less time consuming than a census, less costly to administer than a census and it is possible to obtain statistical results of a sufficiently high precision based on samples. There are two types of sampling techniques, probability and non-probability sampling.
Probability Sampling A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. Humans have long practiced various forms of random selection, such as picking a name out of a hat, or choosing the short straw. These days, we tend to use computers as the mechanism for generating random numbers as the basis for random selection. To produce our results, we combine the responses from the sample in a way which takes account of the selection probabilities. Our aim is that, if the sampling were to be repeated many times, the expected value of the results from the repeated samples would be the same as the result we would get if we surveyed the whole population. Because we know the probability of getting each sample we select, we can also calculate a sampling error for the results. The sampling error tells us the amount of variation in the results due to the sampling alone. It gives a measure of the quality of the sample design, and of the survey results. A simple random sample is a sample in which each member of the population is equally likely to be included. One way to conduct a simple random sample is to assign a number to each element in the population, write these numbers on individual slips of