Population - Any set of people or events from which the sample is selected and to which the study results will generalize.

Sample - A group of people or events drawn from a population. A research study is carried out on a sample from a population. The goal is to be able to find out true facts about the sample that will also be true of the population. In order for the sample to truly reflect the population, you need to have a sample that is representative of the population. The best method to use to obtain a representative sample is to randomly select your sample from the population. A study that has a large, randomly selected sample or a carefully matched sample is said to have external validity.

A non-random sample reduces the external validity of the study. Much medical research is done on the patients one sees in the clinic, this is a non-random sample that is not representative of a larger population and will not generalize. Because it will not generalize is not a fatal flaw in the study. A study with a non-random sample still identifies true facts about the sample and perhaps those findings will be true for others as well. It is best to define your population first, and then obtain a random sample.

The sample size required depends on the requirements of the study and size of the population. As a rule the bigger the better. If the sample is too small then the performance of a few individuals can have a big effect on the data, and render the data less representative of the population.

Sample Selection Methods - There are several methods for drawing random samples. All methods produce good random samples.

Simple random sampling from the population using a Random Number Table (At end of Chapter) or some other random process (slips of paper in a hat).

Stratified random sampling from sub-groups in the population, i.e., to have a random sample of 100 people evenly divided by gender, you would divide population into male and female groups and randomly select 50 from each group.

Proportional sampling to insure maintenance of sub-group proportions, i.e., divide population of the School of Allied Health into male and female groups. Since there are 8 women to every man, in order to have a random sample of 100 people balanced on gender we need to randomly select 80 women and 20 men.

Systematic sampling - drawing every kth person, i.e., to get a random sample of voters you select every 10th person from the Voter Registration Roles at the courthouse.

Cluster sampling - a method to get random samples when the population is large, there are important control variables, and you can only study a small sample (i.e., to get a random sample of 60 administrators of hospitals in the United States, you could group hospitals into clusters based on private/public ownership, and big/medium/small hospitals and then randomly select ten subjects from each cluster. This method is a more elaborate version of stratified sampling.

Hence, yours will be simple random sample and it is a case study if the focus in in one context and place!

It is not just a sample because u include all... It is called a census. :)

First of all, you must define what you mean by "small" in order to be scientific in your approach.

But when your population is sizeable, say 400-500, then you can include all the population in your study. This then is not actually called sampling, but CENSUS. It eliminates sampling errors (Singh and Masuku, 2014)