OUTLINE FOR CHAPTER 8 (Part 2) Significance Testing and the Hybrid Approach to Testing Significance Testing The Component Parts of Significance Testing: State a null hypothesis Make a one-tailed versus two-tailed decision Compute a calculated value Determine the data-based p-value Assessing the likelihood that Ho is true Significance Testing Using "p-less-than" and "p-greater-than" Statements: If the precise p-value can't be computed, the researcher might say something like "p<.001" or "p > .15" (but the number to the right of "<" or ">"  isn't the a-level, because there's no a in significance testing Statements in which p is "bracketed" (e.g., ".01 < p < .05" ) might also come from statistical testing A Popular Hybrid: Significance Testing With an a-Level The simple idea of the "hybrid" approach that combines parts hypothesis testing and significance testing "Marginal significance" and other terms for "near misses" The notion of "highly significant" Reporting fail-to-reject decisions with "p-less-than" statements The "one-sided" alternative to the hybrid approach The 7-, 8-, and 10-step versions of the hybrid approach A Few Warnings The importance of Ho The meaning of the data-based probability The subjective interpretation of p The meaning of "p = .0000" "Resetting" the level of significance