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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 Spring '08
 mckenzie
 Normal Distribution, Statistical hypothesis testing, Mean opinion score, Dr. Farrow

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