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# Systematic Sampling: Definition, Types & Examples

Updated: February 27, 2023

In statistics, sampling is the process of selecting a subset of data from a larger set, called the population. Systematic sampling is one of many different types of sampling that can be used when analyzing and reporting data.

It’s a form of sampling known as interval sampling. It uses a fixed and predetermined range of values to select sample data points. This combination of sampling methods ensures unbiased results.

The benefit of this technique is that it eliminates bias by giving every member of the target population an equal opportunity to be sampled.

This article explains why systematic sampling is useful and how to implement it in your own projects.

KEY TAKEAWAYS

• Systematic sampling is one of the most common types of sampling because it’s easy to implement and relatively inexpensive to execute.
• Systematic sampling is useful when you want to make sure that every member of the population has an equal chance of being sampled.
• Unlike some other types of sampling, systematic sampling is not prone to bias. And it is generally representative of the entire population.
• It’s useful to know that every sampling technique comes with some level of risk. It’s also important to note that systematic sampling is among the most reliable sampling techniques.

## What Is Systematic Sampling?

Systematic sampling is a type of probability sampling that uses a fixed interval to select data points from a larger population.

This interval can be anything—from one person to the 100th person—as long as it’s consistent and predetermined. The selection of each data point will depend on its position in relation to the interval.

The process of systematic sampling is the same for all samples. So the data points will be representative and consistent across samples. Systematic sampling can be useful when you want to make sure that every member of the target population has an equal chance of being sampled.

This way, systematic sampling ensures that your sample isn’t biased. However, it’s important to know that every type of sampling comes with some level of risk. This risk is called sampling error, and it occurs when the sample data is not representative of the entire population.

## How Systematic Sampling Works

When you use a systematic sample, you select regular intervals and then count through the population in order to find each data point.

The first data point is chosen at the beginning of the sample interval. The second data point is found midway through the interval. The third data point is found three-quarters of the way through the interval, and so on.

As you move through the population, you record the number of data points you find. You then use this information to determine where the next data point should be selected. The sample interval is then applied to a random start number to select the next data point.

The process is repeated until the desired sample size is reached. This means that every data point in the sample will have a consistent relationship to every other data point in the sample.

## Types of Systematic Sampling

Simple Random Sampling (SRS): This is the most common type of systematic sampling. In SRS, you select a random starting number and then use the interval from the start number to the number of data points found to select the next data point. This ensures you have an unbiased, random sample.

Stratified Sampling (SS): This type of systematic sampling is used when using members of the population. They are divided into groups or categories. SS is used to ensure that each category is proportionately represented in the sample.

This ensures your sample populations have a natural degree of proportions. That’s the benefit of taking a systematic approach. There may be some sampling variance, but it should be negligible.

Cluster Sampling (CS): This is another form of stratified sampling. In this type, the population is divided into groups or “clusters” and then one or a few data points are sampled from and cluster sampling.

This approach to sampling provides a different sampling frame. This is when compared to the above-mentioned sampling methods.

There are some key benefits to using systematic sampling, including:

• Every member of the population has an equal chance of being sampled. This eliminates sampling bias, which can happen when some members of the population are more likely to be sampled than others.
• The sample data is representative of the entire population. This means that the sample data can be used to make accurate generalizations about the entire population.
• The sample size can be calculated in advance. This means that researchers know how many data points they need to collect in advance, which can help with the budgeting process.
• The sample can be analyzed using the same techniques as any other data set.
• The margin of error is known. This means that researchers can account for the potential for sampling error and make their results more reliable.

Conversely, there are also some drawbacks. For example:

• The sample data is representative of the entire population. Although only if the sample size is large enough to be statistically significant. This means that if you sample too few data points, the sample won’t be representative. It then can’t be used to make accurate generalizations about the entire population.
• The sample size can’t be calculated in advance. This means that researchers don’t know how many data points they need to collect. Which can make the budgeting process more difficult.
• The researchers have to select the data points to sample. This means that the researchers have to make a decision about which data points to sample. And they might be tempted to select data points that are more interesting or relevant than others.

## Examples of Systematic Sampling

Surveys: Surveys are one of the most common types of data collection. They can be useful for measuring all kinds of things, from opinion to lifestyle or purchasing habits.

Public Opinion Polls: These surveys are used to track public opinion. They’re usually conducted after major political or social events, such as elections or natural disasters.

Exit Polls: Exit polls are surveys conducted outside polling stations or places where people have voted. Exit polls are used to survey voters about their voting preferences or intentions. They are also known as pre-election polls or pre-poll surveys.

Experimentation: Experiments are often used to test the effectiveness of different products or services. In this type of research, scientists randomly assign test subjects to different groups and then compare the results.

## Summary

Systematic sampling is a type of probability method of sampling that involves selecting a unit from an ordered frame. The population units get selected according to a fixed pattern, such as every 10th person on the list.

This type of sampling is often used when a complete list of units is not available, but an ordered list can be obtained.

Now that you know how the systematic sampling process works, you can use it to select a representative sample from a population or other metric.

## Systematic Sampling FAQs

Why systematic process is important?

The main reason to use a systematic sampling methodology is to eliminate bias. While non-probability sampling methods are not biased, they’re not as reliable because there’s no way to ensure that every member of the population has an equal chance of being sampled.

Is systematic sampling biased or unbiased?

Systematic biases are in some sampling methods. But as noted above, systematic sampling itself is not biased.

However, it is not always representative of the population sizes either; this is because it is not random. In random sampling, every member of the population has an equal chance of being selected for the sample.

Which sampling method is best for large populations?

In most situations, it’s best to use a random sampling method—like the ones discussed above—when analyzing a population of more than a few thousand individuals.

However, there are exceptions to this rule. For example, when the population is very large and the sample size is high, a non-probability sampling method may be more efficient.

What is the difference between random and systematic sampling?

Random sampling is the selection of data points from a large population with no regard for placement in the list or any other low-risk factor. This is different from systematic sampling, which uses a predefined range or interval to select each data point.

Statistical Sampling