A researcher is interested in how individuals in a certain city feel about the chance of their becoming victims of a crime. To investigate this question, she randomly selects 12 city blocks and then randomly selects 50 people from each block. This method of sampling is known as .

random sampling

Explanation: Random sampling is a part of the sampling technique which each sample has an equal probability of being chosen. A situatuon whereby sample chosen randomly is supposed to be an unbiased representation of the total population. If at all, when sample does not represent the population, then the variation is called a sampling error.

Description: Random sampling is one of the easiest ways of collecting data from the total population. In random sampling, each member of the subset carries an equal opportunity of being part of the sampling process. For example, the total workforce in from is 500 and to conduct a survey, a sample group of 50 employees is selected to do the survey. In this scenario the population is the total number of employees in the firm and the sample group of 50 employees is the sample. Every member of the workforce has the same opportunity of being chosen because all the employees which were chosen to be part of the survey were selected randomly. But, when there is a possibility that the group or the sample does not represent the population as a whole, that means, any random variation is termed as a sampling error.

Another example of a simple random sample would be the names of 25 employees being chosen out of a group of company of 250 employees. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen.

Random sampling is usually used in science to conduct randomized control tests or for blinded experiments.

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