Systematic Sampling vs Random Sampling

In statistical research, the two most prevalent approaches for selecting samples from a population are systematic and random sampling. Each method has advantages and disadvantages, and the decision between them is determined by a variety of factors such as the population's characteristics, research aims, and available resources. In this article, we will learn in detail about difference between systematic sampling and random sampling along with basic introduction about them.

What is Systematic Sampling ?

Systematic sampling is a probability sampling method in which researchers select members of the population at a regular interval (or k) determined in advance. If the population order is random or random-like (e.g., alphabetical), then this method will give you a representative sample that can be used to draw conclusions about your population of interest.

Example of Systematic Sampling

For example, if you had a list of 1,000 customers (your target population) and you wanted to survey 200 of them, your sampling interval would be one-fifth. This means that you would sample every fifth person in your list of 1,000 customers.

What is Random Sampling?

Random sampling is a method of choosing a sample of observations from a population randomly to make assumptions about the population. It is also called probability sampling. Random sampling methods include simple random sampling, stratified random sampling, and cluster random sampling.

Example of Random Sampling

To ensure a random sample, researchers typically use a random start, e.g. a number within the range of the sampling interval. So you might start with the 2nd name in the list and then sample every 5th person (e.g. 2, 7, 12, 17 and so on).

Differences Between Systematic Sampling and Random Sampling

The difference between systematic sampling and random sampling is tabulated below:

Learn More,

• Systematic Random Sampling
• Simple Random Sampling