Types of Data Sampling Techniques

There are mainly two types of Data Sampling techniques which are further divided into 4 sub-categories each. They are as follows:

Probability Data Sampling Technique

Probability Data Sampling technique involves selecting data points from a dataset in such a way that every data point has an equal chance of being chosen. Probability sampling techniques ensure that the sample is representative of the population from which it is drawn, making it possible to generalize the findings from the sample to the entire population with a known level of confidence.

1. Simple Random Sampling: In Simple random sampling, every dataset has an equal chance or probability of being selected. For eg. Selection of head or tail. Both of the outcomes of the event have equal probabilities of getting selected.
2. Systematic Sampling: In Systematic sampling, a regular interval is chosen each after which the dataset continues for sampling. It is more easier and regular than the previous method of sampling and reduces inefficiency while improving the speed. For eg. In a series of 10 numbers, we have a sampling after every 2nd number. Here we use the process of Systematic sampling.
3. Stratified Sampling: In Stratified sampling, we follow the strategy of divide & conquer. We opt for the strategy of dividing into groups on the basis of similar properties and then perform sampling. This ensures better accuracy. For eg. In a workplace data, the total number of employees is divided among men and women.
4. Cluster Sampling: Cluster sampling is more or less like stratified sampling. However in cluster sampling we choose random data and form it in groups, whereas in stratified we use strata, or an orderly division takes place in the latter. For eg. Picking up users of different networks from a total combination of users.

Non-Probability Data Sampling

Non-probability data sampling means that the selection happens on a non-random basis, and it depends on the individual as to which data does it want to pick. There is no random selection and every selection is made by a thought and an idea behind it.

1. Convenience Sampling: As the name suggests, the data checker selects the data based on his/her convenience. It may choose the data sets that would require lesser calculations, and save time while bringing results at par with probability data sampling technique. For eg. Dataset involving recruitment of people in IT Industry, where the convenience would be to choose the data which is the latest one, and the one which encompasses youngsters more.
2. Voluntary Response Sampling: As the name suggests, this sampling method depends on the voluntary response of the audience for the data. For eg. If a survey is being conducted on types of Blood groups found in majority at a particular place, and the people who are willing to take part in this survey, and then if the data sampling is conducted, it will be referred to as the voluntary response sampling.
3. Purposive Sampling: The Sampling method that involves a special purpose falls under purposive sampling. For eg. If we need to tackle the need of education, we may conduct a survey in the rural areas and then create a dataset based on people’s responses. Such type of sampling is called Purposive Sampling.
4. Snowball Sampling: Snowball sampling technique takes place via contacts. For eg. If we wish to conduct a survey on the people living in slum areas, and one person contacts us to the other and so on, it is called a process of snowball sampling.

Data sampling is a fundamental statistical method used in various fields to extract meaningful insights from large datasets. By analyzing a subset of data, researchers can draw conclusions about the entire population with accuracy and efficiency.

This article will explore the concept of data sampling, its importance, techniques, process, advantages, disadvantages, and best practices for effective implementation.