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# Random Sampling

Random sampling is when every single element in an entire population has an equal probability of being selected. Commonly, random sampling is referred to as simple random sampling or SRS for short. In statistics, random sampling is the most basic sampling method.

When practicing random sampling on a population, the first step entails compiling a list of all the possible subjects and numbering them off. Then a certain number of subjects will be randomly chosen and observed. For example, if you have a population of 200 individuals and must sample 20 people, then you would number everyone from 1 to 200 and randomly select 20 numbers.

When conducting a random sample, technology is often utilized. A random number generator on a computer or specific functions on a graphing calculator can be used to simply select numbers. If technology is not available, more simplistic methods, such as pulling numbers out of a hat can also be employed.

Although random sampling is very basic, it can be difficult to carry out on an extremely large scale, especially when little information on a population is known. For example, if you wanted to randomly sample a particular bird species in the rainforest, but didn't know that total population size and all the locations where birds could be found, then random sampling could be tricky and produce invalid results.

Another potential problem with random sampling is that it may lead to clustered results. For example, pretend you had to select 10 people out of a total population of 50, and 5 of the numbers chosen were between 30 and 39. In this case, the total variation of numbers selected is fairly low. Other sampling methods, such as systematic sampling, avoid this type of issue.

Overall, random sampling is a very straightforward sampling method. In comparison to other sampling methods, it requires little preparation and if implemented properly, the results obtained will not be biased.

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