Statistics

====Sampling====
When full census data cannot be collected, statisticians collect sample data by developing specific [[design of experiments|experiment designs]] and [[survey sampling|survey samples]]. Statistics itself also provides tools for prediction and forecasting through [[statistical model]]s.

To use a sample as a guide to an entire population, it is important that it truly represents the overall population. Representative [[sampling (statistics)|sampling]] assures that inferences and conclusions can safely extend from the sample to the population as a whole. A major problem lies in determining the extent that the sample chosen is actually representative. Statistics offers methods to estimate and correct for any bias within the sample and data collection procedures. There are also methods of experimental design that can lessen these issues at the outset of a study, strengthening its capability to discern truths about the population.

Sampling theory is part of the [[mathematics|mathematical discipline]] of [[probability theory]]. Probability is used in [[statistical theory|mathematical statistics]] to study the [[sampling distribution]]s of [[sample statistic]]s and, more generally, the properties of [[statistical decision theory|statistical procedures]]. The use of any statistical method is valid when the system or population under consideration satisfies the assumptions of the method. The difference in point of view between classic probability theory and sampling theory is, roughly, that probability theory starts from the given parameters of a total population to [[deductive reasoning|deduce]] probabilities that pertain to samples. Statistical inference, however, moves in the opposite direction—[[inductive reasoning|inductively inferring]] from samples to the parameters of a larger or total population.