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One-Sample Means

  • Roy Sabo
  • Edward Boone
Chapter

Abstract

In Chapter 5 we were introduced to continuous data, and in this Chapter we take our first steps into the realm of inference by focusing on hypothesis testing and estimating confidence intervals for one-sample continuous data. In many ways, the material we introduce here will resemble the material covered in Chapter 2, where we focused on the case of a one-sample proportion. While the specific details will clearly be different, most of the steps we take will be the same in both cases. For instance, in both cases we will summarize our data, generate hypotheses, evaluate the veracity of our assumptions, perform the statistical test, and make inference from the output of that test. Most importantly, since we are only dealing with one sample, we must again pay close attention to the hypothesized value upon which we base our test. Before we get to such a test, we must examine the behavior of the sample mean, much as we did for the sample proportion in Chapter 2.

Keywords

Central Limit Theorem Standard Normal Distribution Sample Size Increase Glasgow Coma Scale Score Average Standard Deviation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Roy Sabo
    • 1
  • Edward Boone
    • 2
  1. 1.Department of BiostatisticsVirginia Commonwealth UniversityRichmondUSA
  2. 2.Department of Statistical Sciences and ORVirginia Commonwealth UniversityRichmondUSA

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