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This 3 page Class Notes was uploaded by Debra Tee on Tuesday September 27, 2016. The Class Notes belongs to STATS 250 at University of Michigan taught by Brenda Gunderson in Fall 2016. Since its upload, it has received 3 views. For similar materials see Introduction to Statistics in Statistics at University of Michigan.
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Date Created: 09/27/16
Lecture 3: Sampling: Surveys and How to Ask Questions 5.1 Collecting and Using Sample Data Wisely Definitions: - Descriptive Statistics: Describing data using numerical summaries (such as the mean, IQR, etc.) and graphical summaries (such as histograms, bar charts, etc.). - Inferential Statistics: Using sample information to make conclusions about a larger group of items/individuals than just those in the sample. - Population: The entire group of items/individuals that we want information about, about which inferences are to be made. - Sample: The smaller group, the part of the population we actually examine in order to gather information. - Variable: The characteristic of the items or individuals that we want to learn about. Fundamental Rule for Using Data for Inference: - Available data can be used to make inferences about a much larger group if the data can be considered to be representative with regard to the question(s) of interest. Sample vs Census survey - Many times we cannot sample the whole population, so we have to sample certain people and gauge the percentage of the entire population who have a certain trait or opinion to within 3%. - The key is to use a proper sampling method. Bias: How Surveys can go Wrong - Biased if the method used to obtain those results would consistently produce values that are either too high or too low. - Selection bias occurs if the method for selecting the participants produces a sample that does not represent the population of interest. - Nonparticipation bias (nonresponse bias) occurs when a representative sample is chosen for a survey, but a subset cannot be contacted or does not respond. - Biased response or response bias occurs when participants respond differently from how they truly feel. The way questions are worded, the way the interviewer behaves, as well as many other factors might lead an individual to provide false information. 5.2 Margin of Error, Confidence Intervals, and Sample Size - Survey is used to find a proportion based on a representative sample from the population of interest. The measure of accuracy, how close the proportion comes to the truth of the entire population is called the margin of error Conservative (approximate 95%) Margin of Error =1/sqrt(n) where n is the sample size. Approximate 95% Confidence Interval for p: sample proportion ± 1/sqrt(n) - > p-hat ± 1/sqrt(n)
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