Simple random sampling: Difference between revisions
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Simple random sampling (SRS) is the easiest way to take a sample from some population. The main idea is that everyone has exactly the same chance of being in the sample. Theoretically, then, the sample should be a good representation of the population. | '''Simple random sampling''' (SRS) is the easiest way to take a sample from some population. The main idea is that everyone has exactly the same chance of being in the sample. Theoretically, then, the sample should be a good representation of the population. | ||
Here is a simple example of getting a simple random sample. Suppose there is a club made up of one thousand people. If there is a complete membership list, the easiest way to get a simple random sample is to list all of the members, put a random number next to each member, then sort all of the members by the random numbers. The members are now in random order. To get a simple random sample, just take the first set of members, however many are desired. | Here is a simple example of getting a simple random sample. Suppose there is a club made up of one thousand people. If there is a complete membership list, the easiest way to get a simple random sample is to list all of the members, put a random number next to each member, then sort all of the members by the random numbers. The members are now in random order. To get a simple random sample, just take the first set of members, however many are desired. | ||
Simple random sampling is simple, but in some cases, particularly where the sample is small, the result may not be representative, just due to the luck of the draw. Other methods may often be preferred. | Simple random sampling is simple, but in some cases, particularly where the sample is small, the result may not be representative, just due to the luck of the draw. Other methods may often be preferred. | ||
Latest revision as of 01:35, 28 February 2009
Simple random sampling (SRS) is the easiest way to take a sample from some population. The main idea is that everyone has exactly the same chance of being in the sample. Theoretically, then, the sample should be a good representation of the population.
Here is a simple example of getting a simple random sample. Suppose there is a club made up of one thousand people. If there is a complete membership list, the easiest way to get a simple random sample is to list all of the members, put a random number next to each member, then sort all of the members by the random numbers. The members are now in random order. To get a simple random sample, just take the first set of members, however many are desired.
Simple random sampling is simple, but in some cases, particularly where the sample is small, the result may not be representative, just due to the luck of the draw. Other methods may often be preferred.