Central Limit Theorem .edu
Central Limit Theorem
General Idea: Regardless of the population distribution model, as the sample size increases, the sample mean tends to be normally distributed around the population mean, and its standard deviation shrinks as n increases.
Certain conditions must be met to use the CLT. The samples must be independent The sample size must be "big enough"
CLT Conditions
Independent Samples Test
"Randomization": Each sample should represent a random sample from the population, or at least follow the population distribution.
"10% Rule": The sample size must not be bigger than 10% of the entire population.
Large Enough Sample Size
Sample size n should be large enough so that np10 and nq10
Example: Is CLT appropriate?
It is believed that nearsightedness affects about 8% of all children. 194 incoming children have their eyesight tested. Can the CLT be used in this situation?
Randomization: We have to assume there isn't some factor in the region that makes it more likely these kids have vision problems.
10% Rule: The population is "all children" - this is in the millions. 194 is less than 10% of the population.
np=194*.08=15.52, nq=194*.92=176.48
We have to make one assumption when using the CLT in this situation.
Central Limit Theorem (Sample Mean)
X, 1
X, 2
...,
X n
are
n
random
variables
that
are
independent and identically distributed with
mean and standard deviation .
X
=
(X +X +...+X )/n
12
n
is
the
sample
mean
We can show E(X)= and SD(X)=/n
CLT states:
X -
/n
N
0,1
as n
Implication of CLT
We have:
X -
/n
N
0,1
Which means X N , 2/ n
So the sample mean can be approximated with a normal random variable with mean and standard deviation n.
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