Random Sampling

The random selection of sample participants to derive conclusions and assumptions about an entire population

What is Random Sampling?

Random sampling, or probability sampling, is a sampling method that allows for the randomization of sample selection, i.e., each sample has the same probability as other samples to be selected to serve as a representation of an entire population.

 

Random Sampling

 

Random sampling is considered one of the most popular and simple data collection methods in research fields (probability and statistics, mathematics, etc.). It allows for unbiased data collection, which lets studies arrive at unbiased conclusions.

 

Summary

  • Random sampling, also known as probability sampling, is a sampling method that allows for the randomization of sample selection.
  • It is essential to keep in mind that samples do not always produce an accurate representation of a population in its entirety; hence, any variations are referred to as sampling errors.
  • There are four primary, random (probability) sampling methods – simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

 

Types of Random Sampling Methods

There are four primary, random (probability) sampling methods. These methods are:

 

1. Simple random sampling

Simple random sampling is the randomized selection of a small segment of individuals or members from a whole population. It provides each individual or member of a population with an equal and fair probability of being chosen. The simple random sampling method is one of the most convenient and simple sample selection techniques.

 

2. Systematic sampling

Systematic sampling is the selection of specific individuals or members from an entire population. The selection often follows a predetermined interval (k). The systematic sampling method is comparable to the simple random sampling method; however, it is less complicated to conduct.

 

3. Stratified sampling

Stratified sampling, which includes the partitioning of a population into subclasses with notable distinctions and variances. The stratified sampling method is useful, as it allows the researcher to make more reliable and informed conclusions by confirming that each respective subclass has been adequately represented in the selected sample.

 

4. Cluster sampling

Cluster sampling, which, similar to the stratified sampling method, includes dividing a population into subclasses. Each of the subclasses should portray comparable characteristics to the entire selected sample. This method entails the random selection of a whole subclass, as opposed to the sampling of members from each subclass. This method is ideal for studies that involve widely spread populations.

 

Practical Example

A company currently employs 850 individuals. The company wishes to conduct a survey to determine employee satisfaction based on a few identified variables. The research team decides to have the sample set at 85 employees. The 85 employees will be part of the survey and will be used as a representation for the total population of 850 employees.

In such a scenario, the sample is the 85 employees, and the population is the entire workforce consisting of 850 individuals. Based on the sample size, any employee from the workforce can be selected for the survey. It goes to say that each employee has an equivalent probability of being randomly selected for the survey.

It is important to keep in mind that samples do not always produce an accurate representation of a population in its entirety; hence, any variations are referred to as sampling errors. A sampling error can be defined as the difference between the respective statistics (sample values) and parameters (population values). The sampling error is inevitable when sample data is being used.

 

Why an Unbiased Random Sample Matters

Unbiased random sampling results in more reliable and unbiased conclusions.

For example, the employee satisfaction survey mentioned above makes use of a sample size of 85 employees. Of these employees, it is possible to have selected more females than males for the study, despite the entire workforce having 450 men and 400 women. It would result in a sampling error, as it causes variations in the results obtained. Ideally, results should be objective and unbiased.

 

Probability (Random) Sampling vs. Non-Probability Sampling

Probability – or random sampling – is the random selection of sample participants to derive conclusions and assumptions about an entire population. On the other hand, non-probability sampling is the selection of sample participants based on specified criteria or suitability.

 

More Resources

CFI is the official provider of the global Commercial Banking & Credit Analyst (CBCA)™ certification program, designed to help anyone become a world-class financial analyst. To keep advancing your career, the additional CFI resources below will be useful:

  • Basic Statistics Concepts in Finance
  • Inferential Statistics
  • Parameter
  • Sampling Distribution

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