Under simple random sampling, a sample of items is chosen randomly from a population, and each item has an equal probability of being chosen. Simple random sampling uses a table of random numbers or an electronic random number generator to select items for its sample. For example, the lottery operates based on a simple random sampling, with all numbers having an equal probability of getting chosen. Meanwhile, systematic sampling involves selecting items from an ordered population using a skip or sampling interval. That means that every "nth" data sample is chosen in a large data set. The use of systematic sampling is more appropriate compared to simple random sampling when a project's budget is tight and requires simplicity in execution and understanding the results of a study. Systematic sampling is better than random sampling when data does not exhibit patterns and there is a low risk of data manipulation by a researcher, as it is also often a cheaper and more straightforward sampling method.
- 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.
- While systematic sampling is easier to execute than simple random sampling, it can produce skewed results if the data set exhibits patterns. It is also more easily manipulated.
- On the contrary, simple random sampling is best used for smaller data sets and can produce more representative results.
Simple random sampling requires that each element of the population be separately identified and selected, while systematic sampling relies on a sampling interval rule to select all individuals. If the population size is small or the size of the individual samples and their number are relatively small, random sampling provides the best results since all candidates have an equal chance of being chosen. However, as the required sample size increases and a researcher needs to create multiple samples from the population, this can be very time-consuming and expensive. As a result, systematic sampling becomes a preferred method under such circumstances.
Systematic sampling is better than simple random sampling when there is no pattern in the data. However, if the population is not random, a researcher runs the risk of selecting elements for the sample that exhibit the same characteristics. For instance, if every eighth widget in a factory was damaged due to a certain malfunctioning machine, a researcher is more likely to select these broken widgets with systematic sampling than with simple random sampling, resulting in a biased sample.
When deciding when would you use systematic sampling, it's important to consider that there is always a risk of manipulation that poses a threat to running an informative and clear study. In that vein, in cases where is a low risk of data manipulation, systematic sampling is preferable to simple random sampling for its ease of use. However, if such a risk is high when a researcher can manipulate the interval length to obtain desired results—for example, being able to change every 100th number being pulled in a systematic sample—a simple random sampling technique would be more appropriate.