What is Probability Sampling? Definition, Methods

Research can be of several types, such as market research, scientific research, etc. And when research is to be conducted, one of the important things that are required is data. Data proves to be beneficial as it leads to the understanding of the confidential information of any subject. Often data is collected from different sources and different people. If the research is focused on a group of people, then collecting data from everyone is not a possible task. In such cases, a sample of people is selected to represent the group and help in the research process.

The sample selected should represent the group well to ensure effective drawing out of conclusions from the results. Therefore, the decision to select the method of sampling is quite important in the research study. Broadly, there are two ways of sampling, which are probability sampling and non-probability sampling.

The probability sampling method involves the random selection of samples, whereas, in the case of the non-probability sampling method, non-random selection methods are used for sampling.

The article will focus on the methods of probability sampling.

Before understanding the concept of the sampling method, it is best to get an idea of what a sample and population means.

  • Population refers to the entire group of individuals for which the researcher wants to draw certain conclusions.
  • Sample refers to the specific group of people or individuals collected from the population and the data is collected.
  • Various characteristics are considered while defining a population, such as age, geographical location, income, etc.
  • Based on the research of the study, the target population should be defined well.
  • A good sample representing the population becomes difficult to form when the population size is considered too large.
  • Terms used in Sampling Methods

A few terms are mostly used in sampling methods, such as the sampling frame and the sample size.

  • Sampling size: The sampling size refers to the size of the sample. This means the number of individuals that are considered within a sample. Including people in a sample depends on various factors, such as the variability and the size of the population. It also depends on the design of the research.
  • Sampling frame: It is defined as the list of individuals that will form the actual sample.

Probability Sampling

The method of sampling that selects out a sample from a population is referred to as probability sampling. This means that the sample is chosen at random or by chance. The process of this type of sampling is more time-consuming and costly.

In probability sampling, as the sample is chosen randomly by chance, every member or individual of every population has the probability of being a part of the sample. That means every member has the chance of being selected in the sample.

Suppose any user or researcher wants to carry out the study over a group of individuals that would represent the characteristics of the overall population. In that case, the probability sampling method is considered the best choice.

Types of Probability Sampling Methods 

The probability sampling methods are classified further into five different types of sampling methods.

1. Simple random sampling

The first group of sampling methods is the simple random sampling method. In this sampling method, the members within a population have all the same chance of being selected. 

The sampling frame should be the whole actual population.

Tools that you can use in this sampling method are random number generators or other tools that consider techniques based on chance.

  • Example of simple random sampling

Suppose a sample of 100 employees is to be chosen from a group of employees in an organization. In that case, the numbers from 1 to 100 can be randomly distributed to the employees. Then, through a random number generator, 100 numbers are selected out from the distributed numbers.

2. Systematic sampling

The process of sampling method is similar to simple random sampling. However, this method is considered a more straightforward process than the previously mentioned method. In this method, every member within a population is listed with a numerical entity. However, the numbers that are assigned to the individuals are not randomly chosen. Instead, they are given numbers at a regular interval.

  • Example of systematic sampling

Suppose 20 numbers of individuals are to be selected out from a group of 100 people. In such cases, when we apply systematic sampling, the numbers are assigned to the individuals systematically. While selecting out the individuals, a random number is selected out at the start. Once the starting number is decided, the next number goes on at certain intervals, such as 8, 18, 28, etc. Likewise, the 20 people can be selected out systematically.

While using the systematic sampling technique, it should be noted that there should not be any hidden patterns existing in the list of individuals.

3. Stratified sampling

Unlike the earlier discussed methods, in this method, the population is at first divided into sub-population. As the population gets divided, these small groups become important in some way. The stratified sampling helps in getting more specific conclusions related to the study. This is because the method ensures that every subgroup is appropriately represented in the considered sample while sampling.

The process starts with the division of the population into definite sub-groups or strata. These sub-groups can be formed based on characteristics such as age, job, salary, etc. Once it has been divided, based on the population under study, any sampling method can be applied to form a sample representing each sub-population. 

4. Cluster sampling

The method of cluster sampling includes the formation of a subpopulation from a bigger population. The only difference between the stratified sampling and cluster sampling is that each subgroup generated should have characteristics similar to each other. As similar characteristics are present in each sub-group, you can select the entire sub-group randomly instead of sampling individuals from the sub-groups. For reduction of the cost, this type of method can be selected out by the statisticians.

The cluster samples form “pockets” for the sampled units rather than spreading the sample over the whole population. This reduces the costs for the operations involved in collections. There might be another reason why cluster sampling should be used. This is because, in the case of other sampling methods, the unit list for the population might not be available. On the other hand, in the case of cluster sampling, the cluster list can be created easily or is available.  

However, cluster sampling has a drawback as it is less efficient than the simple random sampling method. Because of this, the survey should be conducted for a large number of clusters of smaller sizes rather than surveying a small number of clusters of larger sizes. Another drawback of the cluster sampling method that has been reported is that there is no control on the final size of the sample.

5. Multi-stage sampling 

The method is almost similar to the cluster sampling method. However, the difference lies in forming a sample where a sample is selected out from each cluster rather than the whole cluster. There are two stages present in this sampling method. In the first stage, a large number of clusters are identified and then selected. The second stage of the method includes the selection of units from the created clusters. This can be done through the use of any of the types of probability sampling methods. Therefore, in the multi-stage screening process, the clusters formed are the primary sampling units, i.e., PSU.

In contrast, the units that are present within the cluster are termed secondary units of sampling. More stages of sampling can be present in this type of sampling method. In those cases, tertiary sampling units are selected, and the process continues until the final sample is formed.

Advantages of Probability Sampling

The probability sampling methods consist of different techniques which provide different benefits. The single method has its unique advantage. The list of advantages has been mentioned below.

  • The cluster sampling method is quite easy to use and convenient.
  • The method of simple random sampling leads to the creation of samples that can represent the whole population.
  • The stratified sampling method leads to the creation of layers of the population that represent the whole population.
  • Samples can be easily formed without using any tools for random number generation in systematic sampling methods.

Conclusion 

Probability sampling is a type of sampling method that helps select a sample from a population. One of the important goals in selecting a sample through probability sampling is to minimize sampling errors for the estimates. In addition, it should be noted that the cost of the survey should be reduced along with the time taken to conduct a survey. In this article, we discussed the different methods that are included in probability sampling.

If you’re interested to learn more about artificial intelligence & machine learning, check out IIIT-B & upGrad’s Online Machine Learning & AI Program which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

Enhance Your Career in Machine Learning and Artificial Intelligence

0 replies on “What is Probability Sampling? Definition, Methods”

×