Week 7 Study Notes

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13 Dec 2010
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GGR 270 Lecture 7 October 27, 2010
Central Limit Theorem II
The frequency of sample means will be normally distributed
When the sample size is large, the sample mean is likely to be quite close to the
population mean
A large sample is more likely to be closer to the true population mean than a smaller
sample
Variability
Standard deviation of the sampling distribution is equal to the sample standard
deviation divided by the square root of the sample size
This is called the standard error of the mean
oIndicates how much a typical sample mean is likely to differ from the true
population mean
oMeasures the amount of sampling error
oThe larger the sample size (n), the smaller the amount of sampling error
How large is large
If sampled population is normal, then sampling distribution of means will also be
normal, no matter what the sample size
If the sampled population is approximately normal, then the sampling distribution of
means will be approximately normal for relatively small sample sizes
When the population is skewed, the sample size must be large (n>30) before the
sampling distribution will become normal
Sample Estimation
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