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

by OC10403

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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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