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Chapter17 - Testing for Differences Between Two Groups or...

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Testing for Differences Between Two Groups or Among More than Two Groups
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Ch 17 2 Why Differences are Important Market segmentation holds that within a market, there are different types of consumers who have different requirements, and these differences can be the bases of marketing strategies.
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Ch 17 3 Why Differences are Important Some differences are obvious – differences between teens’ and baby boomers’ music preferences. Other differences are not so obvious and marketers who “discover” these subtle differences may take advantage of huge gains in the marketplace.
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Ch 17 4 Why Differences are Important Market Segmentation Differences must be statistically significant Statistical significance of differences: the differences in the sample(s) may be assumed to exist in the population(s) from which the random samples are drawn
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Ch 17 5 Why Differences are Important Market Segmentation Differences must be meaningful Meaningful difference: one that the marketing manager can potentially use as a basis for marketing decisions
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Ch 17 6 Why Differences are Important Market Segmentation Differences should be stable Stable difference: one that will be in place for the foreseeable future Differences must be actionable Actionable difference: the marketer can focus various marketing strategies and tactics, such as advertising, on the market segments to accentuate the differences between segments
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Ch 17 7 Small Sample Sizes: The Use of a t Test or a z Test Most of the equations in this chapter will lead to the computation of a z value. There are certain circumstances in which the z test is not appropriate. The t -test should be used when the sample size is 30 or less. The t -test is defined as the statistical inference test to be used with small sample sizes (n is < or = to 30).
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Ch 17 8 Determining Statistical Significance: The P value Statistical tests generate some critical value usually identified by some letter; i.e., z , t or F. Associated with the value will be a p value which stands for probability of supporting the null hypothesis (no difference or no association). If the probability of supporting the null hypothesis is low, say 0.05 or less, we have significance!
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