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Probability and Samples - Coggle Diagram
Probability and Samples
Distribution of sample means- the collection of sample means for all the possible random samples of a particular size that can be obtained from a population
sample error- difference or amount of error, between a sample statistic and its corresponding population parameter
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Central Limit Theorem- provides a precise description of the distribution that would be obtained if you selected every possible sample mean, and constructed the distribution of the sample mean
1) describes the distribution of sample means for any population no matter what shape mean or standard deviation
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describes the distribution of sample means by identifying the 3 basic characteristics that describe any distribution: shape, central tendency and variability
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Mean- expected value of M- a sample mean is expected to be near its population mean, average value is identical to standard deviation
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z-Scores for a sample mean- a signed number that identifies the location of the sample mean in the distribution of sample means so that:
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2- number tells the distance between the sample mean and standard deviation in terms of standard errors
Sampling error- a sample typically will not provide a perfectly accurate representation of its population (will be same discrepancy between the mean for a sample and the mean of the population from which the sample was obtained)
Standard error- provides a way to measure the average or standard deviation between a sample mean and the population mean