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ENTR 3P99 (5)
Chapter 9

Chapter 9

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Department
Entrepreneurship
Course
ENTR 3P99
Professor
Dirk De Clercq
Semester
Winter

Description
CHAPTER 9SAMPLING ISSUES Populations and SamplesPopulations and Parameters y a Population is the entire collection of all observations of interest people objects or events as defined by the researchery A parameter is a descriptive measure of the entire population of all observations of interest to the researcher Eg mean of a population variable Samples and statistics Since it is not usually possible or practical to measure every individual person or event a census we take A Sample as a representative portion of the population which is selected for studyA Statistic is a characteristic of the sample used to estimate the equivalent population parameterGeneralizing from sample to population Logically the greater the similarity between our small sample and the large population the more representative the sample is of the population the better the sample statistic should estimate the population parameter Inferential StatisticsInferential Statistics are used to provide the probability that a pattern found in your sample data will actually be true for your populationThe term inferential comes from the word inferThat is from the results of our sample what is the probability that we can infer the same sort of result for the larger populationSurvey vs Census SURVEY Systematic process of gathering information from a sample in order to study a population CENSUS A survey in which all of the population elements are includedConcept of Sampling The concept of sampling therefore involvestaking elements of the defined population according to acceptable procedures to ensure a representative samplemaking observationsassessments on this smaller group of elements then generalizing findings back to the defined population from which the representative sample was drawn Sampling Error the difference between the unknown population parameter and the sample statistic being used to estimate the parameter Sampling Error There are at least two possible causes of sampling error 1 Sampling error is mere chance in the sampling process 2 Sampling bias occurs when there is some tendency in the sampling process to select certain sample elements over others For example the sampling process may favour the selection of males to the exclusion of females married persons to the exclusion of singles or older employees rather than younger onesSampling Distribution This is a distribution of all possible values of a statistic eg means of all samples of the same size selected from a population is the sampling distribution of sample meansThe mean of the sampling distribution of all the means of all samples of the same size n 30 from the population approximates to the population mean
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