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NOTES: Statistics and Society 113 Vocab and Samples:Chapters 1-4Vocabulary:Individuals: objects described by a set of dataVariable: any characteristic of an individual, different valuesProportion: # of successes = X total sample size nPopulation: the entire groups of individuals about which we want informationConsensus: attempts to get information from every member of the population (time consuming and expensive).Sample: a part of the population that we examine in order to gather information about the whole population.Parameter: number that is true for the whole populationStatistic: number that is true for the sampleBias: consistent, repeated deviation of the sample statistic from the population parameter in the same direction when we take many samples (choosing a random sample will reduce bias).Variability: how spread out the sampling distribution is for the statistic. Determined by sampling design and sample size “n” (larger samples have smaller variability).Types of Samples:Convenience Sample: NOT A RANDOM SAMPLE, NOT THE BEST SAMPLESelection of which individuals are easiest to reach, ex: Mall Surveys (Anyone is given a survey)Voluntary Response Sample: NOT A RANDOM SAMPLE, NOT THE BEST SAMPLEConsists of people who choose themselves by responding to a general appealBiased because people with strong opinions are most likely to respond, often negative responses, ex: Restaurant Surveys (How was your service? People with bad service are more likely to fill out the survey)Random Sample: RANDOMEliminates biasGives individuals equal chance to be chosenTwo types
o Simple Random (SRS) o Stratified Random_____________________________________________________________________________________Simple Random (SRS):The one we’ll be using in STAT 113Have a list of the whole population, then use a random method to select our sample, each individual has equal chance of being chosenStratified Random:Divide the individuals from the population into groups based on some characteristic, then take simple random samples within each of these groups, combine all of those samples into one big sampleProblems with Samples:Random sampling error: deviation between the sample statistic and the population parameter caused by chance in selecting a random sampleo Each time you take a random sample from the population, you will get a slightly different statistic, due to random variability**Taking a larger sample will help reduce random sampling error**Undercoverage: occurs when some groups in the population are left out of the process of choosing the sampleResponse error/bias: occurs when a subject gives an incorrect response (lying, remembering incorrectly, doesn’t understand the question, etc.)Nonresponse: the failure to obtain data from an individual selected for a sample. Usually happens because some subjects can’t be contacted or because those who are contacted refuse to cooperateHow to deal with nonsampling errors:Substitute other households for the nonresponders, hopefully from the same neighborhoodOnce the responses are in, statistical methods can be used to weight the responses to correct the biasHow to determine: How good is a survey?Who carried out the survey?What was the population?
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