2. Part Four ANALYSIS AND PRESENTATION OF DATA
McGraw-Hill/Irwin
© 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved.

3. Chapter Seventeen HYPOTHESIS TESTING

4. Approaches to Hypothesis Testing
Classical Statistics
sampling-theory approach
objective view of probability
decision making rests on analysis of available sampling data
Bayesian Statistics
extension of classical statistics
consider all other available information

5. Types of Hypotheses Null Alternative
that no statistically significant difference exists between the parameter and the statistic being compared
Alternative
logical opposite of the null hypothesis
that a statistically significant difference does exist between the parameter and the statistic being compared.

6. Logic of Hypothesis Testing
Two tailed test
nondirectional test
considers two possibilities
One tailed test
directional test
places entire probability of an unlikely outcome to the tail specified by the alternative hypothesis

7. Decision Errors in Testing
Type I error
a true null hypothesis is rejected
Type II error
one fails to reject a false null hypothesis

8. Testing for Statistical Significance
State the null hypothesis
Choose the statistical test
Select the desired level of significance
Compute the calculated difference value
Obtain the critical value
Interpret the test

9. Classes of Significance Tests
Parametric tests
Z or t test is used to determine the statistical significance between a sample distribution mean and a population parameter
Assumptions:
independent observations
normal distributions
populations have equal variances
at least interval data measurement scale

10. Classes of Significance Tests
Nonparametric tests
Chi-square test is used for situations in which a test for differences between samples is required
Assumptions
independent observations for some tests
normal distribution not necessary
homogeneity of variance not necessary
appropriate for nominal and ordinal data, may be used for interval or ratio data

11. How to Test the Null Hypothesis
Analysis of variance (ANOVA)
the statistical method for testing the null hypothesis that means of several populations are equal

12. Multiple Comparison Tests
Multiple comparison procedures
test the difference between each pair of means and indicate significantly different group means at a specified alpha level (<.05)
use group means and incorporate the MSerror term of the F ratio

13. How to Select a Test Which does the test involve?
one sample,
two samples
k samples
If two or k samples,are the individual cases independent or related?
Is the measurement scale nominal, ordinal, interval, or ratio?

14. K Related Samples Test Use when:
The grouping factor has more than two levels
Observations or participants are
matched or
the same participant is measured more than once
Interval or ratio data