Types of probability sampling
Tags: probability
Published: 2023-09-07 07:09:37

Probability sampling assumes that every member of a population has a known and equal chance of being chosen. For example, if you had a population of 100 people, each person would have a 1 in 100 chance of getting chosen. Those odds are not equal when using non-probability sampling. For example, a person may have a better chance of getting picked if they live close to the researcher or have access to a computer. Probability sampling provides the best opportunity to obtain a sample that is truly representative of the population.

Note:  If you are a student and struggling with your Probability Assignment, then you can get the best [Probability Homework Help](https://www.javaassignmenthelp.com/probability-homework-help) from our experts.

5 Types of probability sampling


1. Simple random sampling

Simple random sampling is a sampling approach that selects samples using a table of random numbers or an electronic random number generator. This strategy is useful for obtaining samples from a broad and diverse population.

For example, researchers could collect a large group of people, assign numbers to each person, and then choose numbers at random using an automated approach. To conduct the automated selection procedure, researchers may also employ a lottery system or computer software.



2. Random cluster sampling

Random cluster sampling is a sampling approach that selects samples at random from a large geographic region, such as a state or country's population. Researchers can divide a population into clusters and sample a few of them at random. To make the study procedure more convenient and efficient, the sample is reduced to a tiny fraction of all geographic clusters.

Because in-person sample research may be conducted within a few clusters, this technique allows researchers to analyze a subset of a population with minimal travel. Because a population's geographic area may have hundreds or thousands of clusters, it is typically more convenient to travel to only three or four clusters within a population region.


Note:  If you are a student and struggling with your Excel Assignment, then you can get the best [Excel Homework Help](https://www.javaassignmenthelp.com/excel-homework-help) from our experts.


3. Stratified random sampling

A sampling approach known as stratified random sampling divides a population into smaller subgroups based on common demographic variables such as age, gender, or ethnicity. The researchers then choose a random sample of participants from each grouping. Members of each category have equal chances of becoming study participants when stratified random sampling is used.



4. Systematic sampling

A sampling strategy that uses a numerical interval system to choose samples from a population is known as systematic sampling. Every member of a population can be assigned a number, and individuals can be chosen at regular intervals, such as every fifth person in the sample. Because of this randomization, a survey subset that reflects the variance in the general population can be formed.



Note:  If you are a student and struggling with your Excel Assignment, then you can get the best [Excel Assignment Help](https://www.javaassignmenthelp.com/excel-assignment-help) from our experts.

5. Multi-stage sampling

Multi-stage sampling is a random sampling method that selects a sample in stages, with each stage introducing its own sampling mechanism. For example, researchers may divide a population's location into clusters and then randomly select some of those clusters using random cluster sampling. Then they can sample those random clusters using stratified random sampling, basic random sampling, systematic sampling, or another round of random cluster sampling. The researchers can do more rounds of sampling until they find a final subgroup to analyze.