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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
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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
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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
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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