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Sampling Distributions: Key Concepts and Methods

Learn the basics of sampling distributions, population vs. sample, statistics vs. parameters, and inference techniques for making conclusions and decisions based on samples. Explore confidence intervals and hypothesis testing.

margaret-emerson
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Sampling Distributions: Key Concepts and Methods

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  1. Sampling Distributions8.1 A review of basic methods and terms A preview of the CLT

  2. Big Ideas • Population = All measurements of interest • Sample = A subset of the measurements from the population • Random Sample = A representative sample… a sample that accurately reflects the population… its what we are interested in and worked so hard to get in projects • Draw The Picture!

  3. Sample Statistics and Population Parameters • A Statistic = a numerical descriptive measure of a sample… a # that describes a sample… x bar, s, s squared, p hat • A Paramter = a numerical descriptive measure of the population… a # that describes the pop… mew, little sigma, sigma squared, rho, and others to be learned • Put symbols in the picture

  4. Inference • We use a sample statistics to make inferences (conclusions, decisions) about population parameters when we don’t have access (usually by choice) to all the measurements in the entire population. • We make inferences by: Estimation (chapter 9/ confidence intervals) and Decision (chapters 10+/hypothesis testing)

  5. So, where ya headed ? • Chs 1—4: Descriptive Statistics organize, summarize numbers • Chs 5—8: Probability Theory and Distributions • Chs 9—13: Inferential Statistics…Methods of using a sample to obtain reliable information about the population. We wont be certain that our results absolutely reflect the entire population…however, we will be pretty sure…highly confident…and we can describe likely differences.

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