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6: Sampling Distributions

  • Page ID
    42021
    • Linda R. Cote, Rupa G. Gordon, Chrislyn E. Randell, Judy Schmitt, and Helena Marvin
    • University of Missouri System

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    • 6.1: People, Samples, and Populations
      This page synthesizes concepts for making population inferences from data samples, leading to formal hypothesis testing. It covers individual scores, variability through z-scores, variance, and standard deviation. The chapter explains sampling error and the sampling distribution of sample means, highlighting its normal shape, true population mean, and standard error. Mastery of these principles is essential for effective inferential statistics.
    • 6.2: Two Important Axioms
      This page explores the concepts of sampling distributions, central limit theorem, and law of large numbers, which together show that larger sample sizes lead to more precise population mean estimates and a normal distribution of sample means. It emphasizes the role of standard error in determining probabilities within a normal curve, highlighting how sampling error affects inferential statistics and hypothesis testing, laying the foundation for further discussion on statistical methods.

     


    This page titled 6: Sampling Distributions is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Linda R. Cote, Rupa G. Gordon, Chrislyn E. Randell, Judy Schmitt, and Helena Marvin via source content that was edited to the style and standards of the LibreTexts platform.