The definitions you work with in describing your population of interest can greatly affect the accuracy of your sample. Non-response error is an important consideration. In survey research, it is necessary to assume that the people who respond are not too different from those who choose not to. The question is whether that is a valid assumption or not. Because we don't know, we usually have to qualify our interpretations.
Errors can enter into the sampling process as well.
Names may be duplicated on a list, increasing the likelihood that someone will be selected, or the list may not include everyone it should. Considering the level of accuracy you feel is required for the issues you are investigating. Recall that we are trying to estimate what the population is like by selecting a sample from it. We need a confidence level because we assume there will be some error. Common confidence levels are 95 percent and 99 percent, which means that 95 or 99 times out of 100, the sample we pull will be within the specified error range of the population.