Chapter 7: SamplingDistributionsRead Chapter 8Learning Objectives1.Statistic vs. Parameter2.Sampling Distributions3.Mean and Standard Deviation of the Sampling Distribution of a Proportion4.Standard Error5.Population, Data, and Sampling Distributionslog(AveCorCopies) ~ Time| Stress2ConclusionsWorking Hypothesis:Theoretical Models, Data Generating MechanismsDescriptive StatisticsPopulations and SamplesPopulation– total collection of all individuals of interestSample– randomly selected collection of nindividuals on which observations are made.Data– observations made on a collection of objectsPopulation ÙSampleGoal: We want a representativesample. The sample population needs to represent the target population. Such a sample can be used to make inferences about the population.Sampling Distributions (Rice University)Principles of SamplingExamine a Part of the Whole– the sample. Vegetable Soup example –Instead of eating the whole pot, you taste a spoonful. Randomize – stir up the pot before you sample the spoonful. The top may be salty or not have any potato in it, etc. How big of a sample size do you need?That depends on what you are trying to estimate. If you’re interested in the broth only, then a single sip may do – assuming it has been well stirred/randomized. If you are interested in all the big chunks of vegetables in the soup, you need a large enough sample to make it representative of the whole. For example, assume that your soup has very large pieces of carrots and potatoes- so large in fact that you can not expect to sample a piece of each with a single spoonful. Then you will have to take many samples in order to taste a carrot and potato. The more structure – the larger the sampleLarger absolute sample size, not fraction of population.
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