Definition: Random sampling is a part of the sampling technique in which each sample has an equal probability of being chosen. A sample chosen randomly is meant to be an unbiased representation of the total population. If for some reasons, the sample does not represent the population, the variation is called a sampling error. Description: Random sampling is one of the simplest forms of collecting data from the total population. Under random sampling, each member of the subset carries an equal opportunity of being chosen as a part of the sampling process. For example, the total workforce in organisations is 300 and to conduct a survey, a sample group of 30 employees is selected to do the survey. In this case, the population is the total number of employees in the company and the sample group of 30 employees is the sample. Each member of the workforce has an equal opportunity of being chosen because all the employees which were chosen to be part of the survey were selected randomly. But, there is always a possibility that the group or the sample does not represent the population as a whole, in that case, any random variation is termed as a sampling error. Show An unbiased random sample is important for drawing conclusions. For example when we took out the sample of 30 employees from the total population of 300 employees, there is always a possibility that a researcher might end up picking over 25 men even if the population consists of 200 men and 100 women. Hence, some variations when drawing results can come up, which is known as a sampling error. One of the disadvantages of random sampling is the fact that it requires a complete list of population. For example, if a company wants to carry out a survey and intends to deploy random sampling, in that case, there should be total number of employees and there is a possibility that all the employees are spread across different regions which make the process of survey little difficult.
Sampling is a technique of selecting individual members or a subset of the population to make statistical inferences from them and estimate the characteristics of the whole population. Different sampling methods are widely used by researchers in market research so that they do not need to research the entire population to collect actionable insights. It is also a time-convenient and a cost-effective method and hence forms the basis of any research design. Sampling techniques can be used in a research survey software for optimum derivation. For example, if a drug manufacturer would like to research the adverse side effects of a drug on the country’s population, it is almost impossible to conduct a research study that involves everyone. In this case, the researcher decides a sample of people from each demographic and then researches them, giving him/her indicative feedback on the drug’s behavior. Learn more about Audience by QuestionPro Types of sampling: sampling methodsSampling in market action research is of two types – probability sampling and non-probability sampling. Let’s take a closer look at these two methods of sampling.
In this blog, we discuss the various probability and non-probability sampling methods that you can implement in any market research study. Types of probability sampling with examples:Probability sampling is a sampling technique in which researchers choose samples from a larger population using a method based on the theory of probability. This sampling method considers every member of the population and forms samples based on a fixed process. For example, in a population of 1000 members, every member will have a 1/1000 chance of being selected to be a part of a sample. Probability sampling eliminates sampling bias in the population and gives all members a fair chance to be included in the sample. There are four types of probability sampling techniques:
Uses of probability samplingThere are multiple uses of probability sampling:
Types of non-probability sampling with examplesThe non-probability method is a sampling method that involves a collection of feedback based on a researcher or statistician’s sample selection capabilities and not on a fixed selection process. In most situations, the output of a survey conducted with a non-probable sample leads to skewed results, which may not represent the desired target population. But, there are situations such as the preliminary stages of research or cost constraints for conducting research, where non-probability sampling will be much more useful than the other type. Four types of non-probability sampling explain the purpose of this sampling method in a better manner:
Uses of non-probability samplingNon-probability sampling is used for the following:
How do you decide on the type of sampling to use?For any research, it is essential to choose a sampling method accurately to meet the goals of your study. The effectiveness of your sampling relies on various factors. Here are some steps expert researchers follow to decide the best sampling method.
Unlock the power of accurate sampling! Difference between probability sampling and non-probability sampling methodsWe have looked at the different types of sampling methods above and their subtypes. To encapsulate the whole discussion, though, the significant differences between probability sampling methods and non-probability sampling methods are as below:
ConclusionNow that we have learned how different sampling methods work and are widely used by researchers in market research so that they don’t need to research the entire population to collect actionable insights let’s go over a tool that can help you manage these insights. QuestionPro understands the need for an accurate, timely, and cost-effective method to select the proper sample; that’s why we bring QuestionPro Software, a set of tools that allow you to efficiently select your target audience, manage your insights in an organized, customizable repository and community management for post-survey feedback. Don’t miss the chance to elevate the value of research. Try QuestionPro today! |