Random sampling simply means the sample you chose from a population for a particular statistics must be random and not biased. One way is to have all names of the population to be randomly drawn by a computer system or a manual system (eg. drawing names from a fish bowl). Obtaining statistics information from a supermarket or from a particular group of social group is not random sampling as it is believe people of the same group has the same opinion.
Eg. If you want to do a survey on how often people shop in Walmart, obtaining sample from Walmart shoppers is NOT random sampling because you are only doing survery on those who are already shopping in Walmart. Instead do random survery in a particular work environment unrelated to Walmart or door by door interview as this will allow access to a variety of people including those who never shop in Walmart (a data you cannot obtain from Walmart shoppers)
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The answer depends on how the sample is selected. If it is a simple random sample, of size n, then it is distributed approximately normally with the same mean as the population mean.The answer depends on how the sample is selected. If it is a simple random sample, of size n, then it is distributed approximately normally with the same mean as the population mean.The answer depends on how the sample is selected. If it is a simple random sample, of size n, then it is distributed approximately normally with the same mean as the population mean.The answer depends on how the sample is selected. If it is a simple random sample, of size n, then it is distributed approximately normally with the same mean as the population mean.
A random distribution is a random sample set displayed in the form of a bell curve. See random sample set.
to select a random sample you pick them at random
a random friend put
It is important to make sure your random sample is random in order to make sure the results are accurate, and to prevent experimenter bias.