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Chapter7_1 - Learning Objectives Chapter 7 Sampling...

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Chapter 7: Sampling Distributions Read Chapter 8 Learning Objectives 1. Statistic vs. Parameter 2. Sampling Distributions 3. Mean and Standard Deviation of the Sampling Distribution of a Proportion 4. Standard Error 5. Population, Data, and Sampling Distributions log(AveCorCopies) ~ Time| Stress2 Conclusions Working Hypothesis: Theoretical Models, Data Generating Mechanisms Descriptive Statistics Populations and Samples Population – total collection of all individuals of interest Sample – randomly selected collection of n individuals on which observations are made. Data – observations made on a collection of objects Population Ù Sample Goal: We want a representative sample. 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 Sampling Examine 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 sample Larger absolute sample size, not fraction of population.
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