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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:
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Systematic Sampling
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Random Sampling
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systematic sampling used when a project is on a tight budget or requires a short timeline.
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Random Sampling is Used when project is having good and better Budget and more time to complete
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Systematic sampling relies on a sampling interval rule to select all individuals.
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Simple random sampling requires that each element of the population be separately identified and selected
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Systematic sampling is easier to execute than simple random sampling, it can produce skewed results if the data set exhibits patterns.
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In simple random sampling, each data point has an equal probability of being chosen. Meanwhile, systematic sampling chooses a data point per each predetermined interval.
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It is also more easily manipulated.
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Not Easily Manipulated .
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After a random start, every kth unit is selected
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Each unit has an equal probability of being selected
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Not suitable if periodicity exists in the ordered list
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Not affected by periodicity
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For Probability Sampling Considered a probability sampling method
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For Probability Sampling Considered a probability sampling method (e.g., simple random sampling)
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Potential Biases Possible if the ordered list is not randomized
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Potential Biases Minimizes potential biases if properly implemented
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Systematic sampling is useful when a complete list of the population is available, and the population is homogeneous or does not have a specific pattern or order.
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Random sampling is preferred when it is possible to obtain a complete list of the population and when there is no specific pattern or order in the population.
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Learn More,
FAQs on Systematic Sampling vs Random Sampling
What is systematic sampling?
Systematic sampling is a type of probability sampling method where samples are selected from a population at a fixed periodic interval. For example, selecting every 10th household in a neighborhood is a best example of this .
What is random sampling?
Random sampling is a technique where samples are selected from a population entirely by chance, with each member of the population having an equal probability of being chosen.
Which one is easier to implement?
Systematic sampling is generally easier to implement than compared to random sampling because it does not require generating random numbers or a random starting point. So thus it is very easy.
Which one is more likely to be biased?
Systematic sampling has a higher risk of introducing bias if there is a hidden periodic pattern in the population that corresponds to the sampling interval. Random sampling is less likely to be biased if done correctly.
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