Political Polls With Margin Of Error
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Margin Of Error Formula
What does it really mean when the news anchor says: "The latest polls show Johnson with 51 percent of the margin of error definition vote and Smith with 49 percent, with a 3 percent margin of error"? If there is a 3 percent margin of error, and Johnson leads Smith by only two percentage points, then
Acceptable Margin Of Error
isn't the poll useless? Isn't it equally possible that Smith is winning by one point? The 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 margin of error calculator 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 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 deliv
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 now, let's
Margin Of Error Sample Size
assume you can count with 100% accuracy.) Here's the problem: Running elections costs a lot of
Margin Of Error Sample Size Calculator
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, polls with margin of error and sample size 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. But how many people http://people.howstuffworks.com/political-polling2.htm 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 their favorite color was blue does http://www.robertniles.com/stats/margin.shtml 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 mathematics for an "edge." If you really want to know the gory