  OUTLINE FOR THE 1st HALF OF CHAPTER 7 IN THE 6th EDITION Hypothesis Testing (NOTE: This outline covers pages 131-144 of Ch. 7. A different outline covers pages 144-159 of this chapter.) Introduction The goal in hypothesis testing (HT): Make educated guesses about unknown population parameters Overview of the chapter The 4 preliminary questions that must be answered before HT starts An Ordered List of the 6 Steps A Detailed Look at Each of the 6 Steps . . . Looked At "Out of Order" Step #1: The Null Hypothesis The definition of a null hypothesis . . . and its symbolic representation Where the null hypothesis comes from The notion of Ho positioned on a "continuum of possible values" Examples from real studies Sometimes Ho is set up to be a "no difference" statement; sometimes it's not Ho & the researcher's hunch; they can be the same, opposites, neither Step #6: Deciding What To Do With the Null Hypothesis Two options: Reject Ho or fail-to-reject Ho The different ways researchers talk about having rejected/not rejected Ho Step #2: The Alternative Hypothesis How it's symbolically represented . . . and its necessary connection to Ho The directional (one-sided) and nondirectional (two-sided) option for Ha The directional/nondirectional option and one-tailed vs. two-tailed tests Step #4: Collection and Analysis of Sample Data The basic logic of hypothesis testing: State Ho, then collect data; Reject Ho if data are inconsistent with Ho, otherwise, fail-to-reject Ho Two ways of summarizing the sample data into a single numerical value: 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" 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