In an ideal world, you have data for the full population and can work with the overall distribution however this is rarely the case. Usually you only have a sample of the data to work from, so you use sample statistics such as the mean and standard deviation to approximate the parameters of the full population. The larger the sample, the more accurate your conclusions are going to be.
A common sampling method is to take multiple random samples, with each sample having its own sample mean that you record to form a sampling distribution. With enough samples the sampling distribution takes on a normal shape regardless of the overall population distribution because of the central limit theorem.
Sampling error occurs when a random sample is used to make inferences about a population because information from the full population is not available. Usually the larger the sample the more representative of the population it is, provided an appropriate sampling technique has been used.