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Chapter 12 Inferring from the Data

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Inferring from Data Estimation and Significance testing

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Inferential Statistics Set of statistical procedures that allow us to go beyond the group measured to make statements about the characteristics of a larger group. Estimation Significance testing

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Inferential Statistics Estimation - used to generalize the results from sample to population Significance testing - examines how likely differences between groups and relationships between variables occur by chance

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Estimation Most populations are too large to conduct a census …..So we sample Statistics allow us to “estimate” characteristics back to the population Example: % of Native Americans in Cleveland

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Only possible when we meet 2 assumptions Variables of interest are assumed to have a normal distribution use of a random sample

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Assumption #1 Estimation begins with the assumption that the scores for a particular variable are distributed in the shape of a symmetrical bell (Bell curve) This is called normal distribution

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Assumption #2 Random sampling-- that each member of the population has an equal chance to be included when inferring back to population… how confident are you when estimating population parameters ? 95% is the accepted level in social sciences

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When trying to answer questions about how variables are related to each other we test for the significance level Significance Testing

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An implicit assumption is built into each hypothesis It predicts that there is no difference between groups and/or no relationship between the variables represents chance occurrence NULL HYPOTHESIS Significance Testing Con’t

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Significance testing--the process of analyzing quantitative data for the purpose of testing whether a null hypothesis is either correct or false We want to reject the null hypothesis Significance Testing Con’t

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Significance level -- the probability level that establishes that we have enough confidence to reject the null hypothesis A 95% or.05 level is standard Significance Testing Con’t

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