Political Polls And Margin Of Error
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Polls With Margin Of Error And Sample Size
Margins of Error Prev Next What does it really mean when the news anchor says: margin of error polls "The latest polls show Johnson with 51 percent of the vote and Smith with 49 percent, with a 3 percent margin of
Margin Of Error Formula
error"? If there is a 3 percent margin of error, and Johnson leads Smith by only two percentage points, then isn't the poll useless? Isn't it equally possible that Smith is winning by one point? The margin of error in polls definition margin of error is one of the least understood aspects of political polling. The confusion begins with the name itself. The official name of the margin of error is the margin of sampling error (MOSE). The margin of sampling error is a statistically proven number based on the size of the sample group [source: American Association for Public Opinion Research]. It has nothing to do with the accuracy of the poll itself. The true margin of error definition margin of error of a political poll is impossible to measure, because there are so many different things that could alter the accuracy of a poll: biased questions, poor analysis, simple math mistakes. Up Next 10 Bizarre Moments in Presidential Elections The Ultimate Political Gaffe Quiz 10 Ways the U.S. Has Kept Citizens From Voting The U.S. Presidential Also-Rans Quiz The U.S. Presidential Debates Quiz Instead, the MOSE is a straightforward equation based solely on the size of the sample group (assuming that the total population is 10,000 or greater) [source: AAPOR]. As a rule, the larger the sample group, the smaller the margin of error. For example, a sample size of 100 respondents has a MOSE of +/- 10 percentage points, which is pretty huge. A sample of 1,000 respondents, however, has a MOSE of +/- 3 percentage points. To achieve a MOSE of +/- 1 percentage point, you need a sample of at least 5,000 respondents [source: AAPOR]. Most political polls aim for 1,000 respondents, because it delivers the most accurate results with the fewest calls. Let's get back to our tight political race between Johnson and Smith. Does a 2-percent lead mean anything in a poll with a 3 percent margin of sampling error? Not really. In fact, it's worse than you think. The marg
accurate, assuming you counted the votes correctly. (By the way, there's a whole other topic in math that describes the errors people can make when they try to measure things like that. But, for
Acceptable Margin Of Error
now, let's assume you can count with 100% accuracy.) Here's the problem: Running elections costs a margin of error calculator lot of money. It's simply not practical to conduct a public election every time you want to test a new product or ad
Margin Of Error Sample Size
campaign. So companies, campaigns and news organizations ask a randomly selected small number of people instead. The idea is that you're surveying a sample of people who will accurately represent the beliefs or opinions of the entire population. http://people.howstuffworks.com/political-polling2.htm 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 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 http://www.robertniles.com/stats/margin.shtml 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 describes the relationship I just mentioned is basically this: The margin of error in a sample = 1 divided by the square root of the number of people in the sample How did someone come up with that formula, you ask? Like most formulas in statistics, this one can trace its roots back to pathetic gamblers who were so desperate to hit the jackpot that they'd even stoop to mathematic
engineering, see Tolerance (engineering). For the eponymous movie, see Margin for error (film). The top portion charts probability density against actual percentage, showing the relative probability that the actual percentage is https://en.wikipedia.org/wiki/Margin_of_error realised, based on the sampled percentage. In the bottom portion, each line segment http://www.stats.org/presidential-pollings-margin-for-error/ shows the 95% confidence interval of a sampling (with the margin of error on the left, and unbiased samples on the right). Note the greater the unbiased samples, the smaller the margin of error. The margin of error is a statistic expressing the amount of random sampling error in a survey's results. It margin of asserts a likelihood (not a certainty) that the result from a sample is close to the number one would get if the whole population had been queried. The likelihood of a result being "within the margin of error" is itself a probability, commonly 95%, though other values are sometimes used. The larger the margin of error, the less confidence one should have that the poll's reported results margin of error are close to the true figures; that is, the figures for the whole population. Margin of error applies whenever a population is incompletely sampled. Margin of error is often used in non-survey contexts to indicate observational error in reporting measured quantities. In astronomy, for example, the convention is to report the margin of error as, for example, 4.2421(16) light-years (the distance to Proxima Centauri), with the number in parentheses indicating the expected range of values in the matching digits preceding; in this case, 4.2421(16) is equivalent to 4.2421 ± 0.0016.[1] The latter notation, with the "±", is more commonly seen in most other science and engineering fields. Contents 1 Explanation 2 Concept 2.1 Basic concept 2.2 Calculations assuming random sampling 2.3 Definition 2.4 Different confidence levels 2.5 Maximum and specific margins of error 2.6 Effect of population size 2.7 Other statistics 3 Comparing percentages 4 See also 5 Notes 6 References 7 External links Explanation[edit] The margin of error is usually defined as the "radius" (or half the width) of a confidence interval for a particular statistic from a survey. One example is the percent of people who prefer product A versus product B. When a si
Polls | 2 comments Presidential Polling's Margin for Error by Rebecca Goldin | Oct 14, 2015 | Margin of error, Polls | 2 comments Polls are finding Donald Trump ahead—way ahead—of other candidates running for the Republican nomination for presidency. Based on a recent Pew Research Center poll, CNN practically declared victory for him, noting he got 25 percent of the votes in the survey. The Daily News wrote off Jeb Bush—pointing to his 4 percent support rate. Ben Carson came in at 16 percent; Carly Fiorina and Marco Rubio won 8 percent. Another poll conducted in October by MSNBC/Wall Street Journal/Marist, found Donald Trump has the support of 21 percent of the participating Republicans in New Hampshire– down from 28 percent of respondents in September. Fiorina comes in second, with 16 percent support, up from 6 percent a month ago. The same organization found 24 percent support for Trump in Iowa in October, down from 29 percent last month. Ben Carson, second in the lead in Iowa in this poll, captures 19 percent of the support, down from 22 percent last month. Yet both polls had fewer than 500 participants, resulting in high margins of error (about 5 percent points). When taking the margin of error into consideration, the preferences of Republican voters are far from certain. But first, what is a margin of error (MOE)? It doesn’t measure most kinds of errors that plague many polls and surveys, like biased questions or selecting survey respondents in a way that’s not random. MOE does not measure a mistake, either. When a random sample of all Republicans is taken—a small group of people meant to be chosen randomly from all the possible likely Republican voters—there is always a possibility that the opinions of those in this sample don’t reflect those of the whole population. The MOE is a measurement of how confident we can be that such a survey of the opinions of a small number of people actually reflects the opinions of the whole population. Polls like these may have other major problems than simply sampling error. Quite possibly they haven’t accounted correctly for the demographics among the respondents to the polls. If those who respond are poorer, more likely to be white, less likely to be educated, or even less likely to vote, than those who actually vote, the survey will be biased. But assumin