Simple random sampling is a method used to cull a smaller sample size from a larger population and use it to research and make generalizations about the larger group. It is one of several methods statisticians and researchers use to extract a sample from a larger population; other methods include stratified random sampling and probability sampling. The advantages of a simple random sample include its ease of use and its accurate representation of the larger population.
How a Simple Random Sample Is Generated
Researchers generate a simple random sample by obtaining an exhaustive list of a larger population and then selecting, at random, a certain number of individuals to comprise the sample. With a simple random sample, every member of the larger population has an equal chance of being selected.
Researchers have two ways to generate a simple random sample. One is a manual lottery method. Each member of the larger population group is assigned a number. Next, numbers are drawn at random to comprise the sample group. if a simple random sample were to be taken of 100 students in a high school with a population of 1,000, then every student should have a one in 10 chance of being selected.
The manual lottery method works well for smaller populations, but it isn't feasible for larger ones. In these situations, researchers prefer computer-generated selection. It works via the same principle, but a sophisticated computer system, rather than a human being, assigns numbers and selects them at random.
Room for Error
With a simple random sample, there has to be room for error represented by a plus and minus variance. For example, if in that same high school a survey were to be taken to determine how many students are left-handed, a random sampling can determine that eight out of the 100 sampled are left-handed. The conclusion would be that 8% of the student population of the high school are left-handed, when in fact the global average would be closer to 10%.
The same is true regardless of subject matter. A survey on the percentage of the student population that has green eyes or is physically incapacitated would result in a high mathematical probability based on a simple random survey, but always with a plus or minus variance. The only way to have a 100% accuracy rate would be to survey all 1,000 students which, while possible, would be impractical. (For related reading, see: What percentage of the population do you need in a representative sample?)
Advantages of Random Sampling
Simple random sample advantages include ease of use and accuracy of representation. No easier method exists to extract a research sample from a larger population than simple random sampling. There is no need to divide the population into sub-populations or take any steps further than plucking the number of research subjects needed at random from the larger group. Again, the only requirements are that randomness governs the selection process and that each member of the larger population has an equal probability of selection.
Selecting subjects completely at random from the larger population also yields a sample that is representative of the group being studied. Even sample sizes as small as 40 can exhibit low sampling error when simple random sampling is performed correctly. For any type of research on a population, using a representative sample to make inferences and generalizations about the larger group is critical; a biased sample can lead to incorrect conclusions being drawn about the larger population.
Simple random sampling is as simple as its name indicates, and it is accurate. These two characteristics give simple random sampling a strong advantage over other sampling methods when conducting research on a larger population.
(For related reading, see: What are the disadvantages of using a simple random sample to approximate a larger population?)