In statistics, sampling is when researchers choose a smaller set of items or individuals within a larger group to study. Researchers then predict the characteristics of a whole population based on that sample. Sampling is advantageous to researchers because it allows them to study large groups even when their time and resources are limited.

Systematic sampling allows researchers to take a smaller sample according to a set scheme or system. Systematic sampling by definition is systematic, but there are still systematic sampling advantages and disadvantages.

Systematic Sampling Definition

One systematic sampling definition is that it is used in probability, especially in economics and sociology. To take a sample using systematic sampling, a researcher selects individual items from a group at a random starting point and takes additional items at a standard interval, called the sampling interval. Researchers determine the sampling interval by dividing the population size by the size of their desired sample. Systematic sampling has advantages and disadvantages.

Systematic Sampling Example

One systematic sampling example involves population analysis. If a polling company asked 10,000 people who they voted for in an election, to make their method a systematic sampling example, researchers would have to determine the overall population they would like to compare their sample to. Those researchers would then use that number to come up with a sampling interval. So in taking their sample, they might not ask every person who they voted for. The might ask every fifth person instead.

Systematic Sampling Advantages and Disadvantages

The pros and cons of systematic sampling include, on the pros side, the simplicity of systematic sampling. Cons include the fact that this method can induce accidental patterns like the overrepresentation of certain characteristics from a population.

Systematic Sampling: Advantages

Creating a systematic sample is relatively easy. Compared with random sampling, it also gives researchers a degree of control. It can help eliminate cluster selection. Systematic sampling also has a notably low risk of error and data contamination.

Systematic Sampling: Disadvantages

Systematic sampling becomes difficult when the size of a population cannot be estimated. This makes systematic sampling less likely to be effective in areas like field research on animals. In that kind of scenario, researchers cannot exactly go out into the field and count how many chipmunks live in a five-mile area.

Systematic sampling also needs to be done in populations with natural randomness. Data will become skewed if it is taken from a group that already has a pattern.

Because of the factor of researcher choice in selecting the sampling interval, systematic sampling comes with the possibility of data manipulation and bias.