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Besides simple random sampling, what other sampling methods can be used by data scientists and statisticians?
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Often a simple random sample is not feasible, or at least not practical, so researchers will do their best to use other sampling methods that are likely to result in a representative sample. A few different sampling methods that may be used successfully are:

1.  $\textbf{stratified sampling:}$ subjects are categorized by similar traits, then sam- ple subjects are randomly selected from each category in numbers that are porportional to their numbers in the population. Example: 4 girls are randomly selected and then 6 boys are randomly selected from a population that is 40\% female. Stratified sampling guarantees a representative sample relative to the categories that are used.
2. $\textbf{cluster sampling:}$ there is already a natural categorization of subjects, usually by location. A sample of $\textbf{categories}$ is selected randomly, and $\textbf{every subject}$ in each of the selected categories is part of the sample. Example: randomly select 10 elementary schools in Georgia, then sample every teacher at each of those schools. Cluster sampling is done for the sake of saving time and/or money.
3. $\textbf{systematic sampling:}$ sample every $n$th subject. Example: sample every $1000$th m\&m to weigh and measure. Systematic sampling is common in manufacturing.
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