distribution SamplingDistributions
forSampleMeans
Objectives
Sampling of a sample mean
The mean and standard deviation of
For normally distributed populations
x
The central limit theorem
2
Reminder:Whatisasamplingdistribution?
The sampling distribution of a statistic is the distribution of all
possible values taken by the statistic when all possible samples of a
fixed size n are taken from the population. It is a theoretical idea we
do not actually build it.
The sampling distribution of a statistic is the probability distribution
of that statistic.
3
Samplingdistributionofthesamplemean
We take many random samples of a given size n from a population
with mean and standard deviation .
Some sample means will be above the population mean and some
will be below, making up the sampling distribution.
Sampling
distribution
of x bar
Histogram
of some
sample
averages
For any population with mean and standard deviation :
The mean, or center of the sampling distribution of x , is equal to
the population mean : x = .
The standard deviation of the sampling distribution is /n, where n
is the sample size : x = /n.
Sampling distribution of x bar
/ n
Mean of a sampling distribution of
x
There is no tendency for a sample mean to fall systematically above or
below , even if the distribution of the raw data is skewed. Thus, the mean
of the sampling distribution is an unbiased estimate of the population mean
it will be correct on average in many samples.
Standard deviation of a sampling distribution of
x
The standard deviation of the sampling distribution measures how much the
sample statistic varies from sample to sample. It is smaller than the standard
deviation of the population by a factor of n. Averages are less variable
than individual observations.
6
Fornormallydistributedpopulations
When a variable in a population is normally distributed, the sampling
distribution of x for all possible samples of size n is also normally
distributed.
Sampling distribution
If the population is N( , )
then the sample means
distribution is N( , /n).
Population
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IQscores:populationvs.sample
In a large population of adults, the mean IQ is 112 with standard deviation 20.
Suppose 200 adults are randomly selected for a market research campaign.
The
distribution of the sample mean IQ is:
A) Exactly normal, mean 112, standard deviation 20
B) Approximately normal, mean 112, standard deviation 20
C) Approximately normal, mean 112 , standard deviation 1.414
D) Approximately normal, mean 112, standard deviation 0.1
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Application
Hypokalemia is diagnosed when blood potassium levels are below 3.5mEq/dl.
Lets assume that we know a patient whose measured potassium levels vary
daily according to a normal distribution N( = 3.8, = 0.2).
If only one measurement is made, what is the probability that this patient will be
misdiagnosed with Hypokalemia?
Instead, if measurements are taken on 4 separate days, what is the probability
of a misdiagnosis?
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Thecentrallimittheorem
Central Limit Theorem: When randomly sampling from any
population with mean and standard deviation , when n is large
x
enough, the sampling distribution of
is approximately normal: ~
N( , /n).
Population with
strongly skewed
distribution
Sampling
distribution of
x for n = 2
observations
Sampling
distribution of
x for n = 10
observations
Sampling
distribution of
x for n = 25
observations
http://baike.baidu.com/view/45355.htm?fr=ala0
Incomedistribution
Lets consider the very large database of individual incomes from the Bureau of
Labor Statistics as our population. It is strongly right skewed.
We take 1000 SRSs of 100 incomes, calculate the sample mean for
each, and make a histogram of these 1000 also means.
We take 1000 SRSs of 25 incomes, calculate the sample mean for
each, and make a histogram of these 1000 means.
Which histogram
corresponds to
samples of size
100? 25?
12
Howlargeasamplesize?
It depends on the population distribution. More observations are
required if the population distribution is far from normal.
A sample size of 25 is generally enough to obtain a normal sampling
distribution from a strong skewness or even mild outliers.
A sample size of 40 will typically be good enough to overcome extreme
skewness and outliers.
In many cases, n = 25 isnt a huge sample. Thus,
even for strange population distributions we can
assume a normal sampling distribution of the
mean and work with it to solve problems.
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Samplingdistributions
Atlantic acorn sizes (in cm3)
14
sample of 28 acorns:
12
Frequenc y
10
8
6
4
Describe
the histogram.
What do you assume for the
population distribution?
What
2
0
1.5
3
4.5
6
7.5
Acorn sizes
would be the shape of the sampling distribution of the mean:
For samples of size 5?
For samples of size 15?
For samples of size 50?
9
10.5 More
ImportantPoints
1.
The mean of the sampling distribution of X is the same as the
mean of the population being sampled from. That is,
()
E X =
2.
X
=
X
=
The Standard Deviation of the sampling distribution of
X
is equal
to the standard deviation of the population being sampled from
divided by the square root of the sample size. That is,
X =
n
15
Imp.Points(Cont.)
3.
If the original population is normally distributed, then for any
sample size n the distribution of the sample mean is also normal.
That is,
X ~ N ( ,
4.
) then X ~ N ,
n
If the distribution of the original population is not known, but n is
sufficiently large, the distribution of the sample mean is
approximately normal with mean and standard deviation given as
X ~ N ,
n
This result is known as the central limit theorem (CLT).
16
Furtherproperties
Any linear combination of independent random variables is also
normally distributed.
More generally, the central limit theorem is valid as long as we are
sampling many small random events, even if the events have different
distributions (as long as no one random event dominates the others).
Why is this cool? It explains why the normal distribution is so common.
Example: Height seems to be determined
by a large number of genetic and
environmental factors, like nutrition. The
individuals are genes and environmental
factors. Your height is a mean.
Weibulldistributions
There are many probability distributions beyond the binomial and
normal distributions used to model data in various circumstances.
Weibull distributions are used to model time to failure/product
lifetime and are common in engineering to study product reliability.
Product lifetimes can be measured in units of time, distances, or number of
cycles for example. Some applications include:
Quality control (breaking strength of products and parts, food shelf life)
Maintenance planning (scheduled car revision, airplane maintenance)
Cost analysis and control (number of returns under warranty, delivery time)
Research (materials properties, microbial resistance to treatment)
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Density curves of three members of the Weibull family describing a
different type of product time to failure in manufacturing:
Infant mortality: Many products fail
immediately and the remainders last a
long time. Manufacturers only ship the
products after inspection.
Early failure: Products usually fail
shortly after they are sold. The design
or production must be fixed.
Old-age wear out: Most products
wear out over time, and many fail at
about the same age. This should be
disclosed to customers.