For example when we took out the sample of 30 employees from the total population of 300 employees, there is always a possibility that a researcher might end up picking over 25 men even if the population consists of 200 men and 100 women. But, there is always a possibility that the group or the sample does not represent the population as a whole, in that case, any random variation is termed as a sampling error.Īn unbiased random sample is important for drawing conclusions. Each member of the workforce has an equal opportunity of being chosen because all the employees which were chosen to be part of the survey were selected randomly. In this case, the population is the total number of employees in the company and the sample group of 30 employees is the sample. For example, the total workforce in organisations is 300 and to conduct a survey, a sample group of 30 employees is selected to do the survey. Under random sampling, each member of the subset carries an equal opportunity of being chosen as a part of the sampling process. If for some reasons, the sample does not represent the population, the variation is called a sampling error.ĭescription: Random sampling is one of the simplest forms of collecting data from the total population.

A sample chosen randomly is meant to be an unbiased representation of the total population. Definition: Random sampling is a part of the sampling technique in which each sample has an equal probability of being chosen.

Random synonym t