What is Random Sampling?

Random Sampling is a data collection and analysis method that selects a representative sample from a large population. The name random sampling comes from the fact that the data set chosen is a random selection where each member of the population is given an equal chance of being selected. It is therefore referred to as a probabilistic sampling method. Non-probability sample methods are the opposite, in which not all members of the population have equal chances of being selected. Random sampling should produce results that are representative of the entire population when done correctly. Researchers examine the data and extrapolate conclusions about the population as a whole based on those who responded.

Random Sampling: 3 Characteristics

The following characteristics are common to random sampling:

  1. Respondents from a larger sampling frame are selected at random. It is a subset randomly selected from a larger population. Random selection can be done in a variety of ways. A researcher could assign a randomly generated number to each member of the population, and then select respondents using a random numbers generator or random number tables. This is how many phone surveys work, where random numbers are selected.
  2. Researchers can make conclusions about the entire population by using data from sample groups. Statistics experts typically view random sampling as a good way to get a snapshot of the entire population.
  3. The sample size must be adjusted to the population size. For a study to be scientifically valuable, it is important that surveys of large populations contain enough respondents in order to ensure random and representative sampling. A study of New York City’s residents, for example, would require a much larger sample size than one of Muncie’s residents.

Random Sampling: Uses and Benefits

It is used in many fields, including science and market analysis. Phone surveys, political polling and epidemiology, tracking diseases, gene pool mapping, and tracking public opinions are some of its manifestations. Researchers can also use random sampling to track the academic performance of a school district or region.

Four types of random sampling

The four types of techniques used by statisticians are described below.

  1. 1. Simple Random Sampling: This method uses random techniques to select respondents from a sampling frame. Random number generation is the most common. Each member of the frame is given a random number, and then certain numbers are chosen at random.
  2. 2. Systematic Sampling: This method of probability sampling simplifies random sampling, by selecting respondents at fixed intervals from a population. A statistician could use systematic random sampling instead of random number generators or random number tables to survey each nth individual in a population. They could, for example, assign a number to each person in the sample but select only those whose numbers ended in five.
  3. 3. Cluster Sampling: Cluster Sampling begins by dividing the population into smaller groups, known as clusters. One of these clusters will be chosen to study. This type of sampling relies on the fact that each cluster is designed to have nearly identical characteristics. If a large corporation has seven offices, for example, then one of these offices will be selected to study, and the results extrapolated in order to describe the entire company.
  4. 4. Stratified sample: Similar to cluster sampling, a stratified sample divides respondents into subgroups. In stratified sampling, these groups are deliberately homogenous. Each stratum has a characteristic in common (such as the gender, language spoken or the highest degree of education obtained). They cannot be in more than one group. Researchers stratify their sampling methods when they feel that the sample frame is not representative of the population. Statisticians can produce more useful and relevant data by breaking down the sample frame and weighting certain demographics in order to represent the actual population.