Sampling Terminologies

The process of selecting a sample can be made easier if you know some sampling terminologies related to sampling. Sample is  part of the population and to draw sample from the population you need to decide about the sample size. There is a formula to decide about the sample size. To find out sample size using that formula you need to know the standard deviation, standard error, population size, confidence level and much ore. There are some terms and their explanation that can help you understand the process of sampling in an easy way.

Sample

In an ideal situation the whole population should be taken to study any variable that you want to study. It is unfortunately impossible to study the whole population. In most of the cases population is large and cannot be studied as a whole. A sample is a part of the population that is drawn from the population. It makes the collection of data and its analysis easy and feasible. Sample size is denoted by lowercase letter “n”.

Population

Population is the total entity of people upon which you want to generalize your research. In research population size is denoted by uppercase letter “N”.

Target population

Target population is different from the actual population in one way. Target population can be very large and the researcher might select a small group of that population or people living in one area to make the research easy. For example, the researcher wants to study the effect of one beauty product on elderly ladies. The target population in this case will be all ladies that use that product. This can be very large group spread over several states or even countries. He decides to select a population that is accessible or lives in his region. This will be called as sampling population or population. Hence target population can be very large as compared to the population that the researcher selects for generalizing his findings.

Sampling framework

Sampling framework is the entire population of people, situation, incidents or households from which the researcher has to take the sample. There might be several sampling framework but it is not always possible to draw samples from them. Some frameworks are difficult to research because of social, moral or ethical issues. Sampling framework is that population from which you can draw sample feasibly.

Sampling design

Sampling design is a technique or a procedure to select samples from the target population. The sampling design ensures that each element in the population has an equal and unbiased chances of becoming part of the sample. Sampling design can be nonrandom and, in this case, some compromises are made to get the desired sample.

Standard error

Standard error is the standard deviation of the sampling distribution. Standard error thus describes the chances of error that may occur in the statistic calculated from the sample. As sample could just estimate the characteristics of the population, hence standard error describes the possibility of error in the sampling distribution.

Standard deviation

Standard deviation determines variability of the actual population from which the sample has been drawn. It is denoted by Greek letter sigma and often written as sd. Standard deviation is also the square root of the variance of the population. In statistical terms it can be defined as the deviation of the data points on each side from the mean.

Confidence level

Confidence interval and confidence level are the two terms that are constantly used in sampling and in analyzing the data. Confidence level is the degree or percentage of confidence that the researcher has on the estimates that he has drawn from the sample. It means that higher the percentage of confidence level, greater will be its reliability.

Mean

In statistics mean is the most common type of average that is used. It is used in sampling to describe the characteristics of sampling distribution.

 

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