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Different Sampling Types

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Cluster Sampling- Cluster sampling takes the population and splits them into random groups which are called clusters. Depending on how many people are needed the researcher will choose a couple of random clusters to use in the research. Cluster sampling is used when it is close to impossible to get info of all the population that is trying to be sampled. For example it would be appropriate to use it if you were trying to get a survey on America's grocery stores. You would find all the stores and randomly select grocery stores in order to conduct your research.
Convenience Sample- The sample or group used in this kind of sample are only used because they are easily accessible and don't involve pre-planning. They may not represent the whole population but they are easy to find and are very cheap for researchers. Another pro of this sampling is that it pretty much randomizes the sample on its own. An example on when this is used is if you want the opinion of students on if they liked a class you might choose the first 5 to walk out or the first 5 on the attendance sheet. Basically which on ever is more convenient for you.
Random Sample- The subset of individuals are randomly selected from a population. Because researchers usually cannot obtain data from every single person in a group, a smaller portion is randomly selected to represent the entire group as a whole. The goal of a random sample is to represent the population. You want to use this type of sampling when you cannot get the opinion of the whole population and you want an opinion of what the population would most likely think. For example, if you wanted to see what was a city's favorite food, you randomly select people for your sample.
Stratified sample- Stratified sampling is a probability sampling technique wherein the researcher divides the entire population into different subgroups or strata, then...

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