Hypothesis Testing

    (NOTE: In this outline, the letters "HT" are an abbreviation for "Hypothesis Testing")

  1. Introduction
    1. The goal in HT: Make educated guesses about unknown population parameters
    2. Overview of the chapter:
      1. Consider the 6 steps that make up the simplest possible version of HT
      2. Other objectives: Ho; the "topsy-turvy" nature of HT; warnings about HT
    3. The 4 preliminary questions that must be answered before HT starts
  2. An Ordered List of the 6 Steps
  3. A Detailed Look at Each of the 6 Steps . . . Looked At "Out of Order"
    1. Step #1: The Null Hypothesis
      1. The definition of a null hypothesis . . . and its symbolic representation
      2. Where the null hypothesis comes from
      3. The notion of Ho positioned on a "continuum of possible values"
      4. Examples from real studies
      5. Sometimes Ho is set up to be a "no difference" statement; sometimes it's not
      6. Ho & the researcher's hunch; they can be the same, opposites, neither
    2. Step #6: Deciding What To Do With the Null Hypothesis
      1. Two options: Reject Ho or fail-to-reject Ho
      2. The different ways researchers talk about having rejected/not rejected Ho
    3. Step #2: The Alternative Hypothesis
      1. How it's symbolically represented . . . and its necessary connection to Ho
      2. The directional (one-sided) and nondirectional (two-sided) option for Ha
      3. The directional/nondirectional option and one-tailed vs. two-tailed tests
      4. The notion of an "inexact" null hypothesis
    4. Step #4: Collection and Analysis of Sample Data
      1. The basic logic of hypothesis testing: State Ho, then collect data; Reject Ho if data are inconsistent with Ho, otherwise, fail-to-reject Ho
      2. Two ways of summarizing the sample data into a single numerical value:
        1. Converting the sample data into a standardized number that's called a "calculated value" (or "test statistic"), such as "t = 2.91" or "F = 12.73"
        2. Letting a computer determine p, the probability of having sample data that deviate as much or more from Ho as do the sample data, assuming for the moment that Ho is true

Note: Steps #5 & #3, along with 2 other facets of HT, are covered on the outline for the 2nd half of Chapter 7

Copyright © 2012

Schuyler W. Huck
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