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Stat Exam 2

Stat Exam 2 - Stat Exam 2 Chapter 7(Distribution of Sample...

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Stat Exam 2 Chapter 7 (Distribution of Sample Means) What is sampling error? Sampling error is the discrepancy or amount of error, between a sample statistic and its corresponding population parameter. Sampling error is the variability of a statistic from sample to sample due to chance. It is also referred to as error variance. What is the difference between a sample distribution and a sampling distribution (e.g., the sampling distribution of sample means)? The distribution of sample means is the collection of sample means for all the possible random samples of a particular size (n) that can be obtained from a population. A sampling distribution is a distribution of stats obtained by selecting all the possible samples of a specific size from a pop. Sampling distribution lets us look at sampling error. It shows us where our little sample fits in, in terms of large population. A sampling distribution is the central feature in hypothesis testing, because it shows the frequency of outcomes obtained when sampling from a particular population. This allows us to decide if our outcome is likely to come from that population, or if our sample is from a different population. Sample distribution shows us what we have been dealing with so far. Frequently distribution of raw scores. It can further be defined as items selected at random from a population and used to test hypotheses about the population. What is the Central Limit Theorem, and how does it apply to sampling distributions? Central Limit Theorem - For any population with mean m and standard deviation o, the distribution of sample means for sample size n will have a mean of m and a standard deviation of o/the square root of n and will approach a normal distribution as n approaches infinity. For a sampling distribution, o=o/the square root of n. This calculation allows you to do a sampling distribution with out starting from scratch. What is standard error, and how is it similar to and different from standard deviation? The standard deviation of the distribution of sample means is called the standard error of M. The standard error measures the standard amount of difference between M and m that is reasonable to expect simply by chance. Standard error of M=o =standard distance between M and . *(from notes) standard error tells us how close our sampling mean represents our population mean. The bigger the standard error the more variability you have between all of your means. It is an estimator to see how good a measure of our mean is. Standard error=o =o/the square root of n. Difference: The standard deviation measures the standard distance between a score and the population mean, X- . Whenever you are working with a distribution of scores the standard deviation is the appropriate measure of variability. Standard error, on the other hand, measures the standard distance between a sample mean and the population mean, M- . Whenever you have a question concerning a sample, the standard error is the appropriate measure of variability.

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