OUTLINE FOR CHAPTER
10
Inferences Concerning One or
Two Means
- Introduction
- A needed shift in inferential focus: from correlations to means
- Reasons why multiple chapters are needed to consider inferences
on means
- Inferences Concerning a Single Mean
- The inferential purpose
- Interval Estimation
- Tests concerning a null hypothesis
- The null hypothesis
- The two most popular test procedures: t-tests and z-tests
- Degrees of freedom (df )
- Inferences Concerning Two Means
- Independent vs. correlated samples
- The basic distinction
- Three settings that produce correlated samples
- The sample sizes, if different, provide a signal as to the
kind of samples
- Terminology
- The inferential purpose
- Setting up and testing a null hypothesis
- The general and typical forms of the tested Ho
- Three settings that produce correlated samples
- The three most popular test procedures: t, z, and F
- Interval estimation with two means
- Multiple Dependent Variables
- Results presented within passages of text or in tables
- The Bonferroni and pseudo-Bonferroni adjustment techniques
- Effect Size Assessment and Power Analyses
- Do impressive p-levels signify important findings?
- Options for checking to see if a statistically significant result
has practical significance too
- A computed effect size
- Omega squared & eta squared
- A post hoc power analysis
- Performing a power analysis before data are collected
- Underlying Assumptions
- The four main assumptions . . . and the two discussed most by
applied researchers
- Testing the assumptions (and options if an assumption seems untenable)
- Equal sample sizes and the notion of "robustness"
- Comments
- A nonsignificant result doesnąt mean that the null hypothesis
is true
- Overlapping distributions
- The typical use of t-tests
- Practical significance versus statistical significance
- Type I and Type II errors
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