Systematic sampling is a statistical approach for systematically and randomly selecting a sample from a larger population.

It is commonly used by researchers and analysts to gather data from large, diverse, or difficult-to-reach populations without surveying every individual.

Whether you’re a researcher, analyst, or simply curious about this sampling technique, this article offers insights into its usage for informed decision-making.

### Systematic Sampling

Systematic sampling is a probability sampling method where researchers select members of the population at a predetermined regular interval (or k).

If the population order is random or resembles randomness (e.g., alphabetical), this method will provide a representative sample that can be used to draw conclusions about the population of interest.

Systematic sampling is a probability sampling method where researchers choose elements from a target population by selecting a random starting point and then picking every nth member after a fixed ‘sampling interval.’

### Examples of Systematic Sampling

**Example 1:** Suppose a statistician wants to select a sample from a population of 10,000 people. By choosing every 100th person for sampling, the statistician ensures a systematic selection process. Additionally, sampling intervals can be time-based, such as drawing a new sample every 12 hours.

**Example 2:** In school, when selecting the captain of a sports team, coaches might ask students to call out numbers such as 1-5 (1-n). The coach would then select the students with a specific number, like three, as team captains. This process is non-stressful for both the coach and the players and ensures an equal opportunity for every member of the population to be selected, minimizing sampling bias.

**Example 3:** If you aim to select 1,000 people from a population of 50,000 using systematic sampling, all potential participants must first be listed. After selecting a starting point, you would then choose every 50th person on the list because 50,000 ÷ 1,000 = 50.

For instance, if the starting point is 20, the selected participants would be the 70th, 120th, and so on. If the end of the list is reached and more participants are needed, the count can loop back to the beginning to complete the sample.

### When to Conduct Systematic Sampling

Systematic sampling is a method that mimics many of the randomization benefits of simple random sampling but is slightly easier to conduct.

You can use systematic sampling with a list of the entire population, similar to simple random sampling.

However, unlike simple random sampling, this method is also applicable when you don’t have access to a complete list of your population in advance.

### How to Create a Systematic Sample

You can use the following steps to create a systematic sample:

**Define Your Population:**Identify the group from which you are sampling.**Settle on a Sample Size:**Determine how many subjects you need to sample from the population to get a representative idea of it.**Assign Every Member a Number:**If your group consists of, for example, 10,000 people, assign each individual a unique number.**Decide the Sampling Interval:**Calculate this by dividing the population size by the desired sample size.**Choose a Starting Point:**Select a random number to begin the sampling process.**Identify Members of Your Sample:**Starting from your random number, select every nth member based on your sampling interval. For example, if your starting point is 15 and your sampling interval is 100, your sample members would be 15, 115, 215, and so on.

### Types of Systematic Sampling

**Linear Systematic Sampling**

Linear systematic sampling involves creating a skip pattern that follows a linear path. Instead of selecting every nth member from the population, the selection process follows a predetermined sequence, such as selecting every 5th member, then every 7th member, then every 9th member, and so on.

This method is useful in situations where there is a specific order or sequence to the population, such as geographical locations along a linear path.

**Circular Systematic Sampling**

In circular systematic sampling, the selection process starts again at the same point after reaching the end. Once the sampling interval reaches the last member of the population, it wraps around to the beginning and continues.

This method is often used when the population exhibits cyclical patterns or when there is no clear starting or ending point.

For example, researchers studying tree growth in a forest could use circular systematic sampling by selecting trees at regular intervals along a circular path, ensuring comprehensive coverage of the forest area.

**Systematic Random Sampling**

Systematic random sampling is the classic form where subjects are selected at predetermined intervals.

For instance, if a researcher wants to select a sample of 100 students from a population of 1,000, they could use systematic random sampling by choosing every 10th student from a randomly sorted list.

This approach ensures that each member of the population has an equal chance of being selected while maintaining a systematic sampling pattern.

### Pros of Systematic Sampling

**Simplicity and Convenience** Systematic sampling is extremely simple and convenient for researchers to create, conduct, and analyze samples.

**Efficient Representation** As there’s no need to number each member of a sample, it is better for representing a population in a faster and simpler manner.

**Precision and Objectivity** The samples created are based on precision in member selection and are free from favoritism.

**Reduced Bias** Systematic sampling avoids the high bias potential seen in methods like cluster sampling and stratified sampling, as members are selected at fixed intervals.

**Minimal Risk** The risk involved in systematic sampling is extremely minimal.

**Even Distribution** This technique is beneficial for diverse populations due to the even distribution of members to form a sample.

### Cons of Systematic Sampling

One main disadvantage of the systematic sampling approach is the need to know the size of the population. Without this information, this technique has flaws. However, systematic sampling can still be effective if you are prepared to estimate the interval.

For instance, a film company wants to survey theatergoers about a new movie. Although they don’t know how many people will attend the screening in advance, they can choose to survey every fifth person leaving the theater. The true population size will then be approximately five times the size of the sample.