Because of the difficulties of obtaining information about all units in a population, it is common to use a small, random and representative subset of the population called a sample.
A sample is a smaller, random and representative (group|subset|data set) of the population.
Whenever a sample is used instead of the entire population, we have to accept that our results are merely estimates and therefore have some chance of being incorrect. This is called sampling error.
Any one sample will never be perfect if we're only getting a random sample from a population.
A larger sample should not affect the mean, but would reduce the standard deviation.
While data mining can be used to uncover patterns in data samples, it is important to be aware that: