Data Sampling: What It Is and How It Works

Data have always existed, and there has always been a tendency to analyze them to make optimal decisions, both in business and in government. Over time, the volume of data we generate has increased significantly, so data sampling—i.e., dividing information into smaller subsets—is becoming increasingly important.

Very briefly, data sampling can be defined as various statistical analysis methods used to select, manipulate, and analyze a subset of representative data in order to obtain the necessary information.

However, there are many other aspects related to the concept of data sampling. In this article, we will not only cover what this model is and how it works, but also how it relates to data analysis, as well as the importance of access to quality Big Data training. Let's go!

What Is Data Sampling?

As we mentioned above, data sampling encompasses all statistical methods aimed at creating subsets of data from larger samples of information. This allows data scientists, big data analysts, and other specialized professionals to identify patterns and trends in a larger dataset, but based on smaller samples.

Thanks to data sampling, data specialists can work with a smaller volume of information that is more manageable. Thus, they can build statistical samples faster, on which analytical models can be run.

Defining and analyzing a representative sample is more efficient and economical than analyzing all data or the entire sample population.

Data sampling can be especially useful when working with datasets that are too large to analyze efficiently in their entirety. Therefore, they represent a very valuable set of techniques in the context of software development for Big Data or Data Science.

Nevertheless, it is necessary to consider the possibility of errors, which depends on the size of the sample used. That is, creating smaller subsets of data can reveal important information about a larger set, although analyzing larger samples sometimes yields more accurate results because the data is harder to manipulate and misinterpret.

How Does Data Sampling Work?

Data sampling involves various techniques and methods, so choosing the right one depends on the specific dataset and situation. Sampling can be probability-based, in which case random numbers corresponding to given points in the dataset are used. This method ensures there is no correlation between the points selected for sampling.

On the other hand, there are also non-probability sampling methods. In this case, the approach is based on the judgment of the analyst, who makes the decision about data sampling. In this case, it is harder to extrapolate whether the sample is representative or whether it influences the original dataset.

Data samples can be formed using various methodologies, either probability-based or based on certain criteria predetermined by analysts and researchers.

Once formed, the sample can be used for predictive analysis. For example, a retail company can use data sampling to identify patterns in customer behavior and then use predictive modeling to create the most effective sales strategies. To ensure the reliability of such predictive models, it is important to follow the complete guide to the Software Testing Life Cycle (STLC).

Types of Data Sampling: Probability and Non-Probability

As we mentioned above, there are two types of data sampling: probability and non-probability:

Probability Data Sampling

Within it, there are different types of approaches:

• Simple random sampling. In this case, software is used to randomly select subjects or specific points from the entire statistical sample population.
• Stratified sampling. In stratified sampling, data sets are created based on a common factor of interest to the analyst. Based on this factor or common point, data are randomly selected for each subgroup or subset of data.
• Cluster sampling. A large data population is divided into smaller subsets called clusters. After this division, a random sample is taken from among them.
• Multi-stage sampling. This is a more complex way of cluster data sampling. In this method, the original dataset is also divided into several clusters. However, these clusters are broken down by secondary factors, based on which sampling and analysis are performed.
• Systematic sampling. A sample is created by establishing an interval that allows data to be extracted from the largest population.

Non-Probability Data Sampling

Non-probability data sampling includes the following: 

• Convenience sampling. Data is collected from a group that is easily accessible and suitable for analysis.
• Purposive sampling. The analyst selects data for sampling based on predetermined criteria set either by themselves or by the company they work for.
• Sequential sampling. All relevant data for each subject that meets certain criteria is collected until the desired sample size is covered.
• Quota sampling. The analyst ensures equal representation of all subgroups in the formed data sample.