Hypothesis testing PowerPoint presentation.ppt

Hypothesis testing

1. 17-1

2. 17-2
McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved.
Part Four
ANALYSIS AND
PRESENTATION OF DATA

3. 17-3
Chapter Seventeen
HYPOTHESIS TESTING

4. 17-4
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. 17-5
Types of Hypotheses
•Null
–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. 17-6
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. 17-7
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. 17-8
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. 17-9
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. 17-10
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. 17-11
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. 17-12
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
MS
error term of the F ratio

13. 17-13
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. 17-14
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