In school, while selecting the captain of sports teams, most of our coaches asked us to call out numbers such as 1-5 (1-n) and the students with a random number decided by the coach, for this instance, 3, would be called out to be the captains of different teams. It would be a non-stressful selection process for both the coach as well as the players. Systematic sampling is an extended implementation of the same old probability technique in which each member of the group is selected at regular periods to form a sample. There’s an equal opportunity for every member of a population to be selected using this sampling technique.

For instance, if a local NGO is seeking to form a systematic sample of 500 volunteers from a population of 5000, they can select every 10th person in the population to systematically form a sample.

Other probability sampling techniques like cluster sampling and stratified random sampling can be very unorganized and laborious due to which researchers and statisticians have turned to methods like systematic sampling or simple random sampling for better sampling results. Systematic sampling consumes the least time as it requires selection of sample size and identification of starting point for this sample which needs to be continued at regular intervals to form a sample.

**Steps to form a sample using the Systematic Sampling technique:**

- A defined structural audience needs to be developed for the researcher to start working on the sampling aspect.
- The research in charge must figure out the ideal size of the sample, i.e how many people from the entire population to choose to be a part of the sample.
- The key to precise, reasonable and practical results is a bigger size of the sample.
- Once the number of the sample size is decided, a number must be assigned to each and every member of the sample.
- The interval of this sample needs to be decided that’ll be the standard distance between the elements.
- The example mentioned above suggests that the sample interval should be 10 which is the result the of division of 5000 (N= size of the population) and 500 (n=size of the sample).

Systematic Sampling Formula for interval (i) = N/n = 5000/500 = 10 |

- The researcher needs to select these members who fit the criteria which in this case will be 1 in 10 individuals.
- A number will be randomly chosen as the starting member (r) of the sample and this interval will be added to the random number to keep adding members in the sample. r, r+i, r+2i etc. will be the elements of the sample.

**Systematic Sampling Advantages: **

- It’s extremely simple and convenient for the researchers to create, conduct, analyze samples.
- As there’s no need to number each member of a sample, systematic sampling is better for representing a population in a faster and simpler manner.
- The samples created are based on precision in member selection and free from favoritism.
- In the other methods of probability sampling methods like cluster sampling and stratified sampling, there are chances of the clusters created to be highly biased which is avoided in systematic sampling as the members are at a fixed distance from one another.
- The factor of risk involved in this sampling method is extremely minimal.
- In case there are diverse members of a population, systematic sampling can be beneficial because of the even distribution of members to form a sample.

**When to use Systematic Sampling?**

Let’s take an example where you want to form a sample of 500 individuals out of a population of 5000, you’d have to number each and every person in the population.

Once the numbering is done, the researcher can select a number randomly, for instance, 5. The 5th individual will be the first to be a part of the systematic sample. After that, the 10th member will be added into the sample, so on and so forth (15th, 25th, 35, 45th, and members till 4995).

Here are 4 other situations of when to use Systematic Sampling:

**Budget restrictions:**In comparison to other sampling methods like simple random sampling, this sampling technique is more suitable for situations where there are budget restrictions and also extremely uncomplicated accomplishment of the study.**Uncomplicated implementation:**As systematic sampling depends on the defined sampling intervals to decide the sample, it becomes simple for the researchers and statisticians to manage samples of larger sizes. This is because the time invested in creating samples is minimal and the cost invested is also restricted due to the periodic nature of systematic sampling.**Absence of data pattern:**There are certain data that don’t have an arrangement in place. This data can be analyzed in an unbiased manner using systematic sampling.**Low risk of data manipulation in research:**Systematic sampling is highly productive while conducting research on a broad subject, especially when there’s negligible risk of data manipulation.

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