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This guide covers the concepts of sampling distributions, parameters, and statistics in statistics. It explains how a parameter describes a population while a statistic describes a sample. The SOCS framework is introduced for describing sampling distributions, along with the concepts of bias and unbiased estimates. It highlights that the mean of a sampling distribution equals the true parameter value if unbiased. Larger samples reduce the likelihood of bias and variability, as they result in a smaller spread in the sampling distribution.
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9.1 – Sampling Distributions • Parameter – number that describes the population • Statistic – number that describes a sample. • Sampling Distribution • Describe through SOCS • Bias • Unbiased – mean of sampling distribution is equal to the true value of the parameter being estimated. • Large samples are less likely to contain bias • Variability of a Statistic – described by the spread of its sampling distribution. • Larger samples give smaller spread.
