Sampling is the process of selecting participants for a research project.

Sampling unit or element: This refers to a single thing selected for inclusion in a research project. For example if you sample students from a college, one student would be your sample unit or element.

Population: All the possible units or elements 

Sample A portion of the elements in a population is considered a sample. Any given sample can be part of more than one population. Does this sound like one of those jargons again? Let us unpack this statement by looking at the following example. You and four of your classmates can be a sample of your class, a sample of your university students, a sample of your country and so on. We can therefore define a sample as a more concrete portion of a population or populations if such a term ever exists.

Sampling frame: Is a listing of the elements in a population. In a university’s admissions office, the list of names may include all the students who have been admitted into the college even the ones who never show up or those who have decided to quit. The sample frame is therefore the largest possible sample of a population. That is everything that can be selected from.

A parameter: Is a value associated with a population and can only be estimated in inferential statistical terms using a sample.  Why? This is because a parameter is usually figured in terms of an abstract value. What does this mean? We cannot calculate the mean of a population if we cannot measure and do not know the exact number of units in the population. However since a sample is more concrete, we can use statistics to estimate parameters.  Since these are only estimates that may contain various amounts of error it is usually referred to us inferential statistics.

Sampling error: Is a term used to refer to the extent to which a sample statistics incorrectly estimates a population parameter.

Confidence level: This is a probability associated with the accuracy of an inferential statistic since we do not know exactly what is and what might have not been included in a given population.