In Statistics, sample refers to a set of data collected from a larger set called population. In other words, a sample is a subset of population. A sample can be collected either randomly or through systematic methods.
In data analysis, often the population consists of large data and evaluating them for a specific purpose is a complex & tedious process. Hence statisticians, collect a data sample which is representative of the population. The properties of the sample can then be attributed to that of the population.
The process of selecting this sample from a population is called as Sampling. There are various reasons why sampling is done:
Sampling saves time
In case of destructive testing methods, sampling is useful
In cases when the entire population data cannot be accessed, sampling is useful
There are basically two types of sample depending on how they are selected.
Random Sample – Here the probability of selecting an object from a population is same for all the objects
Non Random Sample – Here the sample is selected based on a certain criteria where all the objects from a population do not have the same probability of selection.