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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

This preview shows half of the first page. to view the full 1 pages of the document. 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
- 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)