Chapter 3Thought Question 1Thought Question 2Sampling TerminologyBias and VariabilitySampling StrategyProportionsMargin of ErrorCase StudySlide 10Slide 11Key ConceptsChapter 3 1Chapter 3What Do Samples Tell Us?Chapter 3 2Thought Question 1During a medical exam, the doctor measures your cholesterol two times. Do you think both measurements would be exactly the same? Why or why not?Chapter 3 3Thought Question 2To estimate the percentage of all adults who have an internet connection in their homes, a properly chosen sample of 1100 adults across the U.S. was sampled, and 60% said “yes”. How close do you think that is to the percentage of the entire country who have an internet connection? Within 30%? 10%? 5%? 1%? Exactly the same?Chapter 3 4Sampling TerminologyParameter–fixed, unknown number that describes the populationStatistic–known value calculated from a sample–a statistic is used to estimate a parameterBias–in repeated samples, the sample statistic consistently misses the population parameter in the same directionVariability–different samples from the same population may yield different values of the sample statisticChapter 3 5Bias and VariabilityConsider shooting arrows at a target:Bias means the archer systematically misses in the same direction. Variability means that the arrows are scattered.Chapter 3 6Sampling StrategyTo reduce bias, use random samplingTo reduce variability, use larger samples–estimates from random samples will be closer to the true values in the population if the samples are larger–how close will they be?margin of errorChapter 3 7The proportion of a population that has some outcome (“success”) is p.The proportion of successes in a sample is measured by the sample proportion:Proportionssample the in nsobservatio of number totalsample the in successes of numberpˆ“p-hat”Chapter 3 8The amount by which the proportion obtained from the sample ( ) will differ from the true population proportion (p) rarely exceeds the margin of error.Margin of ErrorpˆTypical margin of error: 1/sqrt(n)–In 95% of surveys, the sample proportion will not differ from the population proportion by any more than the margin of error. (“95% confidence”)demoChapter 3 9Case Study62% say it should be guaranteedby the governmentsame as in 2000, up 6 points from 1996 31% say it is not the responsibilityof the governmentGuaranteed Health Insurance in the U.S.? New York Times/CBS News Poll, January 2006Chapter 3 10How the Poll was Conducted This New York Times/CBS News poll was based on telephone interviews conducted January 20 through January 25, 2006 with 1,229 adults throughout the United States. The survey has a random sampling error of approx. ±3 percent. Case StudyChapter 3 11Conclusion (Confidence statement) For the proportion of the population who favor guaranteed health insurance, the sample proportion was = .62 (62%) and the margin of error was ±.03 (3%). We can then say that “we are 95% confident that the proportion of the population who favor guaranteed health insurance was between .59 and .65 (59% and 65%).”Case StudypˆChapter 3 12Key ConceptsParameter versus StatisticBias and VariabilityMargin of ErrorConfidence