Transcript Slide 17
Chapter 17
Business Research
Methods
Donald Cooper
Pamela Schindler
Irwin/McGraw-Hill
©The McGraw-Hill Companies, Inc., 2001
Chapter 17
Hypothesis Testing
Irwin/McGraw-Hill
©The McGraw-Hill Companies, Inc., 2001
Approaches to
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Hypothesis Testing
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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
Irwin/McGraw-Hill
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Hypotheses
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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.
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Logic
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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
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Decision
Testing
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Type I error
a true null hypothesis is rejected
Type II error
one fails to reject a false null hypothesis
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Testing for Statistical
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Significance
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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
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Classes
of Significance
Tests
Slide 17 - 6
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
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Classes
of Significance
Tests
Slide 17 - 7
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 only
normal distribution not necessary
homogeneity of variance not necessary
appropriate for nominal and ordinal data, may be used
for interval or ratio data
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How
Nulltitle
Hypothesis
Slide 17 - 8
Analysis of variance (ANOVA)
the statistical method for testing the null
hypothesis that means of several
populations are equal
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Multiple
Tests
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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
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a Test
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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?
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K Related
Samples
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Use when:
The grouping factor has more than two
levels
Observations or subjects are
matched . . . or
the same subject is measured more than
once
Interval or ratio data
Irwin/McGraw-Hill
The McGraw-Hill Companies, Inc., 2001