Articles Dealing With Margin Of Error
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Presidential Poll Margin Of Error
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Margin Of Error In Polls
Register Six Sigma Tools & Templates Sampling/Data Margin of Error and Confidence Levels Made Simple Tweet Margin of Error and Confidence Levels margin of error examples Made Simple Pamela Hunter 9 A survey is a valuable assessment tool in which a sample is selected and information from the sample can then be generalized to a larger population. Surveying has been likened to http://www.usnews.com/news/blogs/data-mine/2014/02/07/does-the-margin-of-error-make-the-jobs-report-meaningless taste-testing soup – a few spoonfuls tell what the whole pot tastes like. The key to the validity of any survey is randomness. Just as the soup must be stirred in order for the few spoonfuls to represent the whole pot, when sampling a population, the group must be stirred before respondents are selected. It is critical that respondents be chosen randomly so that the survey results can be generalized to the whole https://www.isixsigma.com/tools-templates/sampling-data/margin-error-and-confidence-levels-made-simple/ population. How well the sample represents the population is gauged by two important statistics – the survey's margin of error and confidence level. They tell us how well the spoonfuls represent the entire pot. For example, a survey may have a margin of error of plus or minus 3 percent at a 95 percent level of confidence. These terms simply mean that if the survey were conducted 100 times, the data would be within a certain number of percentage points above or below the percentage reported in 95 of the 100 surveys. In other words, Company X surveys customers and finds that 50 percent of the respondents say its customer service is "very good." The confidence level is cited as 95 percent plus or minus 3 percent. This information means that if the survey were conducted 100 times, the percentage who say service is "very good" will range between 47 and 53 percent most (95 percent) of the time. Survey Sample Size Margin of Error Percent* 2,000 2 1,500 3 1,000 3 900 3 800 3 700 4 600 4 500 4 400 5 300 6 200 7 100 10 50 14 *Assumes a 95% level of confidence Sample Size and the Margin of Error Margin of error – the plus or minu
accurate, assuming you counted the votes correctly. (By the way, there's a whole other topic in math that describes the errors people can http://www.robertniles.com/stats/margin.shtml make when they try to measure things like that. But, for now, let's http://www.economist.com/news/finance-and-economics/21588901-american-corporate-profits-seem-have-defied-gravity-margin-error assume you can count with 100% accuracy.) Here's the problem: Running elections costs a lot of money. It's simply not practical to conduct a public election every time you want to test a new product or ad campaign. So companies, campaigns and news organizations ask a randomly selected small number of people margin of instead. The idea is that you're surveying a sample of people who will accurately represent the beliefs or opinions of the entire population. But how many people do you need to ask to get a representative sample? The best way to figure this one is to think about it backwards. Let's say you picked a specific number of people in the United States at random. What margin of error then is the chance that the people you picked do not accurately represent the U.S. population as a whole? For example, what is the chance that the percentage of those people you picked who said their favorite color was blue does not match the percentage of people in the entire U.S. who like blue best? Of course, our little mental exercise here assumes you didn't do anything sneaky like phrase your question in a way to make people more or less likely to pick blue as their favorite color. Like, say, telling people "You know, the color blue has been linked to cancer. Now that I've told you that, what is your favorite color?" That's called a leading question, and it's a big no-no in surveying. Common sense will tell you (if you listen...) that the chance that your sample is off the mark will decrease as you add more people to your sample. In other words, the more people you ask, the more likely you are to get a representative sample. This is easy so far, right? Okay, enough with the common sense. It's time for some math. (insert smirk here) The formula that descri
Events Jobs.Economist.com The Economist Store Timekeeper reading list My SubscriptionSubscribe to The Economist Activate my digital subscription Manage my subscription Renew Log in or register Subscribe Search this site: World politicsPolitics this week United States Britain Europe China Asia Americas Middle East & Africa International Business & financeAll Business & finance Which MBA? EconomicsAll Economics Economics A-Z Markets & data Indicators Science & technologyAll Science & technology Technology Quarterly CultureAll Culture 1843 Magazine Style guide The Economist Quiz BlogsLatest updates Bagehot's notebook Buttonwood's notebook Democracy in America Erasmus Free exchange Game theory Graphic detail Gulliver Prospero The Economist explains DebateEconomist debates Letters to the editor MultimediaEconomist Films Economist Radio Multimedia library The Economist in audio Print editionCurrent issue Previous issues Special reports Politics this week Business this week Leaders KAL's cartoon Obituaries Buttonwood Margin for error American corporate profits seem to have defied gravity Nov 2nd 2013 | From the print edition Add this article to your reading list by clicking this button Tweet THIS time is different. It is one of the oldest mottos in the financial markets. When Japanese shares traded at intimidating multiples of profits in the late 1980s, sceptics were told that Western valuation methods simply did not apply to Tokyo stocks. During the dotcom bubble, bulls laughed at those who worried about the absence of profits, let alone dividends, at some of the hottest technology companies. The new “key metrics”, believers explained, were price-per-user or price-per-click. In this sectionLabour pains Margin for error Of loans and sharks Cream of Devon When education dries up The ra