Sampling in Research (Definition, Importance and Feature)

 Definition

In statistics, sampling is the selection of a subset of individuals from within a statistical population to estimate the characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population in question.

Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger population. The methodology used to sample from a larger population depends on the type of analysis being performed, but it may include simple random sampling or systematic sampling.

The sample should be representative of the population to ensure that we can generalize the findings from the research sample to the population as a whole.

Importance of Sampling

Generally, sampling allows researchers to obtain enough data to answer the research question(s) without having to query the entire population - saving time and money.

Features of Sampling

·         An ideal sample must be representative of the population corresponding to its properties. It should not lack in any characteristic of the population.

·         It must be unbiased and must be obtained by a probability processor random method.

·         It must make the research work more feasible and has the practicability for the research situation.

·         It must yield an accurate result and does not involve errors. The probability of error can be estimated.

·         Sample must be adequate to ensure reliability. A sample having 10% of the whole population is generally adequate.

·         The sample must be comprehensive. It is a quality of sample which is controlled by the specific purpose of the investigation.

·         Sample units must be chosen systematically and objectively.