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This Concept Map, created with IHMC CmapTools, has information related to: SamplingDistributions, statistic two primary statistics for this class x_bar, p_hat estimates population proportion, normal mu=population mean sigma=population standard deviation/sqrt(n) unknown population standard deviation t-distribution, standard error of the mean s.e.(x_bar) formula is s/sqrt(n), parameter is a constant, statistic estimates parameter, statistic has a sampling distribution, x_bar approximate sampling distribution central limit theorem (CLT), central limit theorem (CLT) requires population sigma finite, x_bar as n increases s.e.(x_bar) decreases, statistic evaluated from sample, x_bar as n increases x_bar approaches mu, statistic two primary statistics for this class p_hat, parameter evaluated from population, x_bar approximate sampling distribution normal mu=population mean sigma=population standard deviation/sqrt(n), central limit theorem (CLT) requires population mean mu, statistic is a random variable, x_bar sampling distribution 1: any sample size if population is bell shaped 2: large sample otherwise [large is usually n>=30], central limit theorem (CLT) requires n 'sufficiently large', normal mu=population mean sigma=population standard deviation/sqrt(n) unknown population standard deviation standard error of the mean s.e.(x_bar)