Confidence Interval And Margin Of Error
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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 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 confidence interval standard error 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 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 o
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 realised, based on the standard error margin of error sampled percentage. In the bottom portion, each line segment shows the 95% confidence
Confidence Interval Confidence Level
interval of a sampling (with the margin of error on the left, and unbiased samples on the right). Note the greater
Confidence Interval Standard Deviation
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 asserts a likelihood (not a certainty) that https://www.isixsigma.com/tools-templates/sampling-data/margin-error-and-confidence-levels-made-simple/ 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 are close to the true figures; that is, the figures https://en.wikipedia.org/wiki/Margin_of_error 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 single, global margin of error is reported for a survey, it refers to the maximum margin of error for all repor
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of http://stats.stackexchange.com/questions/22021/how-are-margins-of-error-related-to-confidence-intervals this site About Us Learn more about Stack Overflow the company Business http://inspire.stat.ucla.edu/unit_10/solutions.php Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up confidence interval Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top How are margins of error related to confidence Intervals? up vote 8 down vote favorite 2 Can somebody tell me the difference between margins of error and confidence intervals? On the Internet I see these two meanings getting used interchangeably. Is margin of error it right to say, "Confidence intervals are shown as 1.96 and displayed on the graphs as error margins"? confidence-interval survey polling share|improve this question edited Jan 31 '12 at 19:31 whuber♦ 145k17281540 asked Jan 31 '12 at 15:56 Mintuz 143115 1 Useful discussions on this topic can be found by searching our site. –whuber♦ Jan 31 '12 at 19:30 add a comment| 2 Answers 2 active oldest votes up vote 9 down vote accepted The Internet is full of garbage, as all of us know. It helps to find authoritative sources and focus on them to help resolve such issues. A pamphlet published by the American Statistical Association (attributed to Fritz Scheuren and "thoroughly updated circa 1997") defines the margin of error as a 95% confidence interval (p. 64, at right). In light of this, it is surprising that the Wikipedia article on margin of error uses a different definition, even though it references this pamphlet! Wikipedia writes, 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. ... When a single, global ma
a confidence interval estimate of a population mean: sample size, variability in the population, and confidence level. For each of these quantities separately, explain briefly what happens to the margin of error as that quantity increases. Answer: As sample size increases, the margin of error decreases. As the variability in the population increases, the margin of error increases. As the confidence level increases, the margin of error increases. Incidentally, population variability is not something we can usually control, but more meticulous collection of data can reduce the variability in our measurements. The third of these--the relationship between confidence level and margin of error seems contradictory to many students because they are confusing accuracy (confidence level) and precision (margin of error). If you want to be surer of hitting a target with a spotlight, then you make your spotlight bigger. 2. A survey of 1000 Californians finds reports that 48% are excited by the annual visit of INSPIRE participants to their fair state. Construct a 95% confidence interval on the true proportion of Californians who are excited to be visited by these Statistics teachers. Answer: We first check that the sample size is large enough to apply the normal approximation. The true value of p is unknown, so we can't check that np > 10 and n(1-p) > 10, but we can check this for p-hat, our estimate of p. 1000*.48 = 480 > 10 and 1000*.52 > 10. This means the normal approximation will be good, and we can apply them to calculate a confidence interval for p. .48 +/- 1.96*sqrt(.48*.52/1000) .48 +/- .03096552 (that mysterious 3% margin of error!) (.45, .51) is a 95% CI for the true proportion of all Californians who are excited about the Stats teachers' visit. 3. Since your interval contains values above 50% and therefore does finds that it is plausible that more than half of the state feels this way, there remains a big question mark in your mind. Suppose you decide that you want to refine your estimate of the population proportion and cut the width of your interval in half. Will doubling your sample size do this? How large a sample will be needed to cut your interval width in half? How large a sample will be needed to shrink y