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A sample is that part of a population which is actually observed. In normal scientific practice, we demand that it be selected in such a way as to avoid presenting a biased view of the population. If statistical inference is to be used, there must be a way of assigning known probabilities of selection to each sample. If the probabilities of different samples are all equal, for example, the method is called simple random sampling.

See also: Sampling (statistics)

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Topics: Statistical Sample, Statistical Regularity, Statistical Model...

Statistical regularityStatistical regularity is a notion in statistics that if we throw a thumbtack onto a table once, we would have a hard time predicting whether the point would touch the surface of the table or not. But if we repeat this experiment many times, we will see t	Statistical modelA statistical model is used in applied statistics. Three basic notions are sufficient to describe all statistical models. We choose a statistical unit which we will observe directly. Multiple observations of the same unit over time is called longitudinal	Statistical inferenceThe topics below are usually included in the area of interpreting statistical data . A more formal name for this topic is statistical inference . Statistical assumptions # Likelihood principle # Estimating parameters # Testing statistical hypotheses # Rev
Statistical theoryThe theory of statistics includes a number of topics: Statistical models of the sources of data and typical problem formulation: # Sampling from a finite population #Measuring observational error and refining procedures #Studying statistical relations Pla	Statistical unitIn different statistical disciplines, the statistical unit is the source of a random variable. There are different ways to study a unit and different names applied #We may be interested in a unit because we intend to generalize from observations on a few	Statistical assemblyA statistical assembly is a study of the relationships among the components in a statistical unit that is made of discrete components like organs or machine parts. Much of the observation for statistical assembly requires special preparation of the unit,
Statistical population	Statistical sample	Statistical dispersion
Statistical assumptions	Statistical independence	Statistical mechanics
Statistical physics	Statistical hypothesis testing	Statistical probability

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