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This Concept Map, created with IHMC CmapTools, has information related to: Statistics example, Estimates e.g. Interval estimates, The Normal Distribution can approximate The sample distribution of the mean of x, Random variables can follow The Binomial Distribution, Inferential statistics is based on the notion of Probability, A Population contains Parameters, Probability distributions are characterized by their Variance, Probability distributions include Other distributions, Estimates e.g. Point estimates, Estimates of parameters allow the analyst to make Inferences, Sample statistics are calculated to make Inferences, Errors can be Random errors, Inferential statistics allows the analyst to study A Population, The sample distribution of the mean of x converges to a normal distribution thanks to a result from A Central Limit Theorem, A Sample provides estimates of Parameters, Random variables are described by their Probability distributions, Parameters characterize Probability distributions, Inferential statistics uses A Sample, Parametric techniques are used when populations follow The Normal Distribution, A Sample generates results containing Errors, Inferences are carried out through Hypothesis testing