Margin Of Error Vs Standard Error
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Margin Of Error Definition
more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Cross margin of error excel 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, based on sample data, what do we call our best guess of a population parameter? and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top What is the difference between “margin of error” and “standard error”? up vote 9 down vote favorite 4 Is "margin of error" the
Why Will An Interval Estimate Most Likely Fall Around The Population Mean?
same as "standard error"? A (simple) example to illustrate the difference would be great! definition share|improve this question edited Sep 23 '11 at 18:04 whuber♦ 145k17284544 asked Sep 23 '11 at 17:06 Adhesh Josh 91293357 add a comment| 3 Answers 3 active oldest votes up vote 13 down vote accepted Short answer: they differ by a quantile of the reference (usually, the standard normal) distribution. Long answer: you are estimating a certain population parameter (say, proportion of people with red hair; it may be something far more complicated, from say a logistic regression parameter to the 75th percentile of the gain in achievement scores to whatever). You collect your data, you run your estimation procedure, and the very first thing you look at is the point estimate, the quantity that approximates what you want to learn about your population (the sample proportion of redheads is 7%). Since this is a sample statistic, it is a random variable. As a random variable, it has a (sampling) distribution that can be characterized by m
test AP formulas FAQ AP study guides AP calculators Binomial Chi-square f Dist Hypergeometric Multinomial Negative binomial Normal Poisson t Dist Random numbers Probability Bayes rule Combinations/permutations Factorial Event counter Wizard Graphing Scientific Financial Calculator books AP calculator review Statistics sampling error formula AP study guides Probability Survey sampling Excel Graphing calculators Book reviews Glossary AP practice exam confidence level Problems and solutions Formulas Notation Share with Friends Margin of Error In a confidence interval, the range of values above and
Critical Value
below the sample statistic is called the margin of error. For example, suppose we wanted to know the percentage of adults that exercise daily. We could devise a sample design to ensure that our sample estimate http://stats.stackexchange.com/questions/15981/what-is-the-difference-between-margin-of-error-and-standard-error will not differ from the true population value by more than, say, 5 percent (the margin of error) 90 percent of the time (the confidence level). How to Compute the Margin of Error The margin of error can be defined by either of the following equations. Margin of error = Critical value x Standard deviation of the statistic Margin of error = Critical value x Standard error of the statistic If you know the http://stattrek.com/estimation/margin-of-error.aspx?Tutorial=AP standard deviation of the statistic, use the first equation to compute the margin of error. Otherwise, use the second equation. Previously, we described how to compute the standard deviation and standard error. How to Find the Critical Value The critical value is a factor used to compute the margin of error. This section describes how to find the critical value, when the sampling distribution of the statistic is normal or nearly normal. The central limit theorem states that the sampling distribution of a statistic will be nearly normal, if the sample size is large enough. As a rough guide, many statisticians say that a sample size of 30 is large enough when the population distribution is bell-shaped. But if the original population is badly skewed, has multiple peaks, and/or has outliers, researchers like the sample size to be even larger. When the sampling distribution is nearly normal, the critical value can be expressed as a t score or as a z score. When the sample size is smaller, the critical value should only be expressed as a t statistic. To find the critical value, follow these steps. Compute alpha (α): α = 1 - (confidence level / 100) Find the critical probability (p*): p* = 1 - α/2 To express the critical value as a z score,
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