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Chapter 11

MKT 500 Chapter Notes - Chapter 11: Confidence Interval, Statistical Hypothesis Testing, Statistical Parameter


Department
Marketing
Course Code
MKT 500
Professor
Helene Moore
Chapter
11

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Wk. 8 Chapter 11 Population estimates and hypothesis testing
Lecture on: October 30, 2012
Generalizing a sample’s findings: estimating the population value
- Parameter proper name for population fact
- Parameter estimation generalization process
- How to estimate a population percentage (categorical data):
o Confidence interval range into which the researcher believes the population
parameter falls with an associated degree of confidence
o The confidence interval is always wider for 99% than it is for 95% when the sample
size is the same and variability is equal
o Standard error depends on two factors: 1. Variability, 2. Sample size
- How to estimate a population average (metric data)
o Calculating a confidence interval for an average
Testing hypotheses about percentages or averages
- Hypothesis testing statistical procedure used to support or not support the hypothesis
based on sample information
- Intuitive hypothesis testing (as opposed to statistical hypothesis testing) when someone
uses something he or she has observed to see if it agreed with or goes against the belief
about that topic
- Testing a hypothesis about a percentage
o Null hypothesis formal statement that there is no difference between the
hypothesized population π value and the p value found in our sample
- Testing a directional hypothesis
o “greater than” or “less than” hypothesis
o Critical z value is adjusted downward to 1.64 and 2.33 for 95% and 99% levels of
confidence, respectively
- Testing a hypothesis about an average (metric variable)
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