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Chapter 11

Chapter 11 Population Estimates and Hypothesis Testing.docx

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Department
Marketing
Course
MKT 500
Professor
Tina West
Semester
Winter

Description
MKT500 Marketing Research CHAPTER 11 Population Estimates and Hypothesis Testing THE CONCEPT OF GENERALIZATION  Sample finding: percentage or average or some other value computed with a sample’s data o If follows proper sampling procedures and ensures sample is good representation of target population, sample findings are best estimates of their respective population facts  Population fact: defined as the true value when a census of the population is taken and the value is determined using all members of the population  Generalization: act of estimating a population fact from a sample finding o Form of logic in which an inference about an entire group is made based on some evidence about that group o Generalization can draw conclusions from amount of available evidence  When estimates of population values are made, the sample finding is used as the beginning point, and then a range is computed in which the population value is estimated, or generalized, to fall GENERALIZING A SAMPLE’S FINDINGS: ESTIMATING THE POPULATION VALUE  Parameter estimation: estimation of population values o Population parameters designated by ∏ (percent) or μ (mean or average); sample findings are relegated to p (percent) or x (average or mean) HOW TO ESTIMATE A POPULATION PERCENTAGE (CATEGORICAL DATA)  Confidence interval: range (lower and upper boundary) into which the researcher believes the population parameter falls with an associated degree of confidence (typically 95% or 99%) o General formula for estimation of population percentage is written in notation form √ P = sample percentage Q = 100% - p Z = z value for 95% (±1.96) or 99% (±2.58) level of confidence (alpha equals ether 95% or 99% level of confidence) S = standard error of the percentage p  Standard error: measure of the variability in a population based on the variability found in the sample o Examining formula for standard error of the percentage, size of standard error depends on two factors: (1) variability, denoted as p times q and sample size, n o Standard error of percentage is larger with more variability and smaller with larger samples  Sampling distribution: sample percentages for all samples plotted as a frequency distribution  Generalization procedures are direct linkages between probability sample design and data analysis o When making non-statistical generalization, judgment can be swayed by subjective factors, and may not be consistent o In statistical estimates, formulas are objective and perfectly consistent, and based on accepted statistical concepts HOW TO ESTIMATE A POPULATION AVERAGE (METRIC DATA)  If data metric, population average is an appropriate measure of central tendency: √ P = sample percentage Q = 100% - p Z = z value for 95% (±1.96) or 99% (±2.58) level of confidence (alpha equals ether 95% or 99% level of confidence) Sp= standard error of the percentage  Standard error of average is larger with more va
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